Final Project Wine Quality Analysis
2024-04-28
Problem Statement:
Primary objective is to understand the factors contributing to its overall quality. With a dataset comprising 1599 observations and 12 independent variables, including acidity, sugar content, sulfur dioxide levels, and alcohol concentration.
We aim to investigate how these chemical components influence the wine’s quality rating, which ranges from 0 to 10.
By identifying the key contributors to wine quality, we strive to provide insights that can assist winemakers in enhancing their production processes and crafting superior-quality wines.
Questions:
- Explain the data collection process (10 points)
link:
https://www.kaggle.com/datasets/yasserh/wine-quality-dataset
Updation frequency: Annually
Description:
This dataset pertains to red variations of Portuguese “Vinho Verde” wine. It contains information about the quantities of different chemicals found in the wine and how they influence its overall quality. The dataset can be approached as either a classification or regression task. However, it’s important to note that the classes are ordered and not evenly distributed; for instance, there are more instances of normal-quality wines compared to excellent or poor ones.
Your objective is to use the provided data to predict the quality of wine. This project presents a straightforward yet demanding task: forecasting the quality of wine.
This data frame contains the following columns:
Input variables (based on physicochemical tests):
1 - fixed acidity
2 - volatile acidity
3 - citric acid
4 - residual sugar
5 - chlorides
6 - free sulfur dioxide
7 - total sulfur dioxide
8 - density
9 - pH
10 - sulphates
11 - alcohol
Output variable (based on sensory data):
12 - quality (score between 0 and 10)
Objective: Understand the Dataset & cleanup (if required).
Build classification models to predict the wine quality.
Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.
# Library to read CSV file
library(readr)## Warning: package 'readr' was built under R version 4.3.2# Data visualization libraries
library(ggplot2) # ggplot2 for creating versatile plots## Warning: package 'ggplot2' was built under R version 4.3.2library(ggcorrplot) # ggcorrplot for visualizing correlation matrices## Warning: package 'ggcorrplot' was built under R version 4.3.2library(ggridges) # ggridges for creating ridge plots## Warning: package 'ggridges' was built under R version 4.3.2library(ggvis) # ggvis for interactive web-based visualizations## Warning: package 'ggvis' was built under R version 4.3.2##
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(GGally) # GGally for plot matrix visualizations## Warning: package 'GGally' was built under R version 4.3.2## Registered S3 method overwritten by 'GGally':
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library(readr) # Library to read CSV file
library(ggplot2)
# Load necessary libraries
library(ggplot2)
library(ggcorrplot)
library(caret)
library(scatterplot3d)
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library(lattice)
library(ggplot2)
library(ggridges)
library(ggvis)
library(ggthemes)
library(cowplot)
library(gapminder)## Warning: package 'gapminder' was built under R version 4.3.2library(gganimate)
library(dplyr)
library(tidyverse)
# Additional visualization libraries
library(scatterplot3d) # scatterplot3d for 3D scatter plots
library(SciViews) # SciViews for scientific data visualization
library(car) # car for additional regression plots
library(lattice) # lattice for lattice plots
library(grid) # grid for low-level plotting functions
library(gridExtra) # gridExtra for arranging multiple plots##
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## combinelibrary(RColorBrewer) # RColorBrewer for additional color palettes
library(gapminder) # gapminder for example datasetData Frame: [1] Datatypes [2] Dataframe view
# Read the CSV file
df <- read_csv("C:/Rutgers/Subjects/Spring Sem/Multivariate Analysis/Data/wine.csv")## Rows: 1599 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): quality
## dbl (11): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Retrieve the full column specification
spec(df)## cols(
## `fixed acidity` = col_double(),
## `volatile acidity` = col_double(),
## `citric acid` = col_double(),
## `residual sugar` = col_double(),
## chlorides = col_double(),
## `free sulfur dioxide` = col_double(),
## `total sulfur dioxide` = col_double(),
## density = col_double(),
## pH = col_double(),
## sulphates = col_double(),
## alcohol = col_double(),
## quality = col_character()
## )df## # A tibble: 1,599 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr>*************************2) Exploratory Data Analysis and Visualizations (50 points)********************************
Univarite Analysis:
[1] Free Sulhpur and Sulphur dioxide have data points above the max range as outliers
[2] Density, pH, Sulphate Seem to be equally distrbuted at median
[3] PH seems to be equally distibuted at medium
[4] Scaling would yeild better result as range will be normallized for all the attributes
boxplot(df[,1:5])
boxplot(df[,6:11])
Bivariate Plot :
Wine Quality (Dependent Variable) Bar Plot
[1] As per observation there are 855 records have vaulue “good” and 744 are rated as bad. Target variable seems to have fair distribution
# Count the occurrences of "bad" and "good" in the quality column
quality_counts <- table(df$quality)
# Create a bar plot with count values displayed on top
ggplot(data = NULL, aes(x = names(quality_counts), y = quality_counts)) +
geom_bar(stat = "identity", fill = c("pink", "green")) +
geom_text(aes(label = quality_counts), vjust = -0.5, color = "black", size = 4) + # Add count values on top of bars
labs(title = "Count of Quality Ratings", x = "Quality", y = "Count") +
theme_minimal()## Don't know how to automatically pick scale for object of type <table>.
## Defaulting to continuous.
## Don't know how to automatically pick scale for object of type <table>.
## Defaulting to continuous.
Multivariate Analysis:
I have split our data into 25 batches to apply star visualization. As due to lots of records the visualization was not clear and very ambiguous to deduce any hypothesis.
But only showing 3
Let’s go through the first three plots.
[1] ‘Bad’ is skewed more towards left side, whereas ‘good’ is little contracted from the left side.
[2] ‘Bad’ is little contracted while ‘good’ is quite populated in the bottom
[3] ‘Bad’ seems to be skewed again towards left side whereas ‘good’ has more regular shape and evenly distributed.
Patterns Revealed: As per obeservation there is a possiblity of left skewedness for ‘bad’ occurences. While ‘good’ ones are more evenly distributed and contracted
# Total number of rows in the dataset
total_rows <- nrow(df)
# Calculate the number of rows in each batch
batch_size <- ceiling(total_rows / 25)
# Create a sequence of indices to split the dataset into batches
batch_indices <- seq(1, total_rows, by = batch_size)
# Split the dataset into 25 batches
batches <- lapply(1:(length(batch_indices) - 1), function(i) {
df[batch_indices[i]:batch_indices[i + 1] - 1, ]
})
# Apply the stars() function to each batch and print as star visualization
for (i in seq_along(batches[1:3])) { # Iterate over the first three batches only
cat("\n=============================================\n")
cat("Batch", i, ":\n\n")
print(stars(batches[[i]], labels = batches[[i]]$quality))
cat("\n=============================================\n")
}##
## =============================================
## Batch 1 :
## Var1 Var2
## 1 2.3 18.4
## 2 4.6 18.4
## 3 6.9 18.4
## 4 9.2 18.4
## 5 11.5 18.4
## 6 13.8 18.4
## 7 16.1 18.4
## 8 18.4 18.4
## 9 2.3 16.1
## 10 4.6 16.1
## 11 6.9 16.1
## 12 9.2 16.1
## 13 11.5 16.1
## 14 13.8 16.1
## 15 16.1 16.1
## 16 18.4 16.1
## 17 2.3 13.8
## 18 4.6 13.8
## 19 6.9 13.8
## 20 9.2 13.8
## 21 11.5 13.8
## 22 13.8 13.8
## 23 16.1 13.8
## 24 18.4 13.8
## 25 2.3 11.5
## 26 4.6 11.5
## 27 6.9 11.5
## 28 9.2 11.5
## 29 11.5 11.5
## 30 13.8 11.5
## 31 16.1 11.5
## 32 18.4 11.5
## 33 2.3 9.2
## 34 4.6 9.2
## 35 6.9 9.2
## 36 9.2 9.2
## 37 11.5 9.2
## 38 13.8 9.2
## 39 16.1 9.2
## 40 18.4 9.2
## 41 2.3 6.9
## 42 4.6 6.9
## 43 6.9 6.9
## 44 9.2 6.9
## 45 11.5 6.9
## 46 13.8 6.9
## 47 16.1 6.9
## 48 18.4 6.9
## 49 2.3 4.6
## 50 4.6 4.6
## 51 6.9 4.6
## 52 9.2 4.6
## 53 11.5 4.6
## 54 13.8 4.6
## 55 16.1 4.6
## 56 18.4 4.6
## 57 2.3 2.3
## 58 4.6 2.3
## 59 6.9 2.3
## 60 9.2 2.3
## 61 11.5 2.3
## 62 13.8 2.3
## 63 16.1 2.3
## 64 18.4 2.3
##
## =============================================
##
## =============================================
## Batch 2 :
## Var1 Var2
## 1 2.3 20.7
## 2 4.6 20.7
## 3 6.9 20.7
## 4 9.2 20.7
## 5 11.5 20.7
## 6 13.8 20.7
## 7 16.1 20.7
## 8 18.4 20.7
## 9 2.3 18.4
## 10 4.6 18.4
## 11 6.9 18.4
## 12 9.2 18.4
## 13 11.5 18.4
## 14 13.8 18.4
## 15 16.1 18.4
## 16 18.4 18.4
## 17 2.3 16.1
## 18 4.6 16.1
## 19 6.9 16.1
## 20 9.2 16.1
## 21 11.5 16.1
## 22 13.8 16.1
## 23 16.1 16.1
## 24 18.4 16.1
## 25 2.3 13.8
## 26 4.6 13.8
## 27 6.9 13.8
## 28 9.2 13.8
## 29 11.5 13.8
## 30 13.8 13.8
## 31 16.1 13.8
## 32 18.4 13.8
## 33 2.3 11.5
## 34 4.6 11.5
## 35 6.9 11.5
## 36 9.2 11.5
## 37 11.5 11.5
## 38 13.8 11.5
## 39 16.1 11.5
## 40 18.4 11.5
## 41 2.3 9.2
## 42 4.6 9.2
## 43 6.9 9.2
## 44 9.2 9.2
## 45 11.5 9.2
## 46 13.8 9.2
## 47 16.1 9.2
## 48 18.4 9.2
## 49 2.3 6.9
## 50 4.6 6.9
## 51 6.9 6.9
## 52 9.2 6.9
## 53 11.5 6.9
## 54 13.8 6.9
## 55 16.1 6.9
## 56 18.4 6.9
## 57 2.3 4.6
## 58 4.6 4.6
## 59 6.9 4.6
## 60 9.2 4.6
## 61 11.5 4.6
## 62 13.8 4.6
## 63 16.1 4.6
## 64 18.4 4.6
## 65 2.3 2.3
##
## =============================================
##
## =============================================
## Batch 3 :
## Var1 Var2
## 1 2.3 20.7
## 2 4.6 20.7
## 3 6.9 20.7
## 4 9.2 20.7
## 5 11.5 20.7
## 6 13.8 20.7
## 7 16.1 20.7
## 8 18.4 20.7
## 9 2.3 18.4
## 10 4.6 18.4
## 11 6.9 18.4
## 12 9.2 18.4
## 13 11.5 18.4
## 14 13.8 18.4
## 15 16.1 18.4
## 16 18.4 18.4
## 17 2.3 16.1
## 18 4.6 16.1
## 19 6.9 16.1
## 20 9.2 16.1
## 21 11.5 16.1
## 22 13.8 16.1
## 23 16.1 16.1
## 24 18.4 16.1
## 25 2.3 13.8
## 26 4.6 13.8
## 27 6.9 13.8
## 28 9.2 13.8
## 29 11.5 13.8
## 30 13.8 13.8
## 31 16.1 13.8
## 32 18.4 13.8
## 33 2.3 11.5
## 34 4.6 11.5
## 35 6.9 11.5
## 36 9.2 11.5
## 37 11.5 11.5
## 38 13.8 11.5
## 39 16.1 11.5
## 40 18.4 11.5
## 41 2.3 9.2
## 42 4.6 9.2
## 43 6.9 9.2
## 44 9.2 9.2
## 45 11.5 9.2
## 46 13.8 9.2
## 47 16.1 9.2
## 48 18.4 9.2
## 49 2.3 6.9
## 50 4.6 6.9
## 51 6.9 6.9
## 52 9.2 6.9
## 53 11.5 6.9
## 54 13.8 6.9
## 55 16.1 6.9
## 56 18.4 6.9
## 57 2.3 4.6
## 58 4.6 4.6
## 59 6.9 4.6
## 60 9.2 4.6
## 61 11.5 4.6
## 62 13.8 4.6
## 63 16.1 4.6
## 64 18.4 4.6
## 65 2.3 2.3
##
## =============================================Multivariate Analsyis: As per the correlation plot observe the attributes having a strong correlation based on color theme.
Observations: [1] Fixed acidity has high correlation with: density and citric acid Total Sulphur dioxide and free sulphur have fairly good [2] correlation [3] PH is negatively correlated to fixed acidity. Volatile acid and citric acid have a fair negative correlation
[4] THere are a few more observations and data seems to have highly related attributes. Which incates we can perform deeper analysis
correlation_matrix<- cor(df[,0:11])
ggcorrplot(correlation_matrix,type = "lower", lab = TRUE)
Data Preprocessing:
[1] one hot encoding: To check correlation of these parameters to good and bad qualtiy of wine
# Define a function to identify categorical columns
is_categorical <- function(col_type) {
identical(col_type, "character") # Check if the column type is character
}
# Extract the column names and their data types from the dataframe
col_names <- names(df)
col_types <- sapply(df, class)
# Identify categorical columns in the dataframe
categorical_cols <- col_names[sapply(col_types, is_categorical)]
# Apply one-hot encoding to the categorical columns
dummy_model <- dummyVars(~., data = df[, categorical_cols])
df_categorical_encoded <- predict(dummy_model, newdata = df[, categorical_cols])
# Combine the encoded categorical columns into a single dataframe
df_final <- cbind(df[, !col_names %in% categorical_cols], df_categorical_encoded)
# View the first few rows of the final combined dataframe
head(df_final)## fixed acidity volatile acidity citric acid residual sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free sulfur dioxide total sulfur dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## qualitybad qualitygood
## 1 1 0
## 2 1 0
## 3 1 0
## 4 0 1
## 5 1 0
## 6 1 0Multivariate plot
Now our data into binary variable for good and bad we can clearly observe that:
[1]Quality of good is positively correlated wit alcohol
[2] Whereas bad quality is negatively correlated
[3]Same relation with sulphates
[4]density, total sulfur dioxide and chlorides are neagtively correlated with good quality and vice versa
[5] PH and residual sugar have least correlation to quality of wine
we have identified the features that make wine good and bad.
correlation_matrix<- cor(df_final[,1:13])
ggcorrplot(correlation_matrix,type = "lower", lab = TRUE)
3D Visualization: 3 Varaites
[1] Observation: The matrix elements on the right side seem to be scattered and left ones seem to be clusetered and densely populated.
But it is difficult to come to a conculsion due to difficulty in viewing each element
As per my observation based on 3D Scatter plot
[1] Quality of wine drops when acidity increases
[2] Quality of wine remains consistent with respect to alcohol contet with low acidity
s3d <- scatterplot3d(df$`fixed acidity`, df$alcohol,df$pH,pch=c(1,16)[as.numeric(df$quality)],xlab="fixed acidity", ylab="alcohol", angle=45,zlab="Quality of Wine", lty.hide=2,type="h",y.margin.add=0.1,font.axis=2,font.lab=2)## Warning in scatterplot3d(df$`fixed acidity`, df$alcohol, df$pH, pch = c(1, :
## NAs introduced by coercion
Multivarite Advance analysis:
Obervations :
[1] As per this plot we can see that Alcohol plots for good and bad are not overlapping and shows opposite pattern. This indicates that alcohol variable can be used for further models
[2]Similar for volatile acid but not opposite trend, only slightly deviating, meanignful variable and can be used int further analysis
ggscatmat(df, columns=1:12, color="quality")## Warning in ggscatmat(df, columns = 1:12, color = "quality"): Factor variables
## are omitted in plot
BiVariate Plot:
Obervation:
[1] Alcohol vs Density plot: Density seems to be negatively correlated to alcohol content,
[2] possibility could be density of other contents to wine leading to low quality as high alcohol score has been graded as good quality.
[3] Correlation line: slope seems to be negative, hence negatively correlated.
# Create a data frame from your existing 'df' object
df1 <- df_final[!sapply(df_final, is.function)]
# Attach the 'df' data frame to the search path
attach(df)
df## # A tibble: 1,599 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr># Now you can use variables in 'df' directly without referencing the data frame
plot(df_final$density ~ df_final$alcohol)
abline(lm(df_final$density ~ df_final$alcohol), col = "red")
BiVariate analysis:
volatile acidity vs alcohol:
[1]Comparing correlated variable observed earlier but we can see they both follow a similar pattern as per scatter plot.
# ggplot
ggplot(df, aes(x = alcohol, y = `volatile acidity`)) + geom_point()
Multivariate Analysis:
Volatile acidity vs pH vs Qualtiy=good only
[1] Good quality wine infers to lighter tone of blue color We can see that good quality coffee in the first plot that it drops with increasing volatile acidity but remains unaffectde by pH
Volatile acidity vs pH vs Qualtiy=bad only [2] inverse for bad quality wine, bad quality wine is ranging towards high volatile acidity
ggplot(df_final, aes(x = pH, y = `volatile acidity`)) + geom_point(aes(color = `qualitygood`))
ggplot(df_final, aes(x = pH, y = `volatile acidity`)) + geom_point(aes(color = `qualitybad`))
Univariate:
There are less records for high alcohol content in entire data frame as histogram is skewed (populated) towards left(low value range)
# histogram
ggplot(df, aes(alcohol)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(df, aes(alcohol)) + geom_histogram(aes(fill = after_stat(count)))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Bivariate plot:
[1] As per regression we can determine that alcohol is inversely correlated to volatile acidity as the slope is negative.
[2] We can also confirm on our hypothesis that citric acid is inversely correlated to pH content in wine as per the negative slope
[3] As per regression model for stat_smooth we can detemrine that as the volatile acidity increase wine quality rating will drop further
# regression
ggplot(df, aes(x = alcohol, y = `volatile acidity`)) + geom_point() + geom_smooth(method = lm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(df, aes(x = pH, y = `citric acid`)) + geom_point() + stat_smooth()## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
ggplot(df_final, aes(x = `volatile acidity`, y = `qualitygood`)) + geom_point() + stat_smooth()## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
- Exploratory Data Analysis and Visualizations (50 points)
Part - 2
Statistics test:
x <- dist(scale(df[, c("fixed acidity", "alcohol", "volatile acidity")],
center = FALSE))
as.dist(round(as.matrix(x), 2)[1:15, 1:15])## 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 2 0.33
## 3 0.12 0.22
## 4 0.88 1.15 0.95
## 5 0.00 0.33 0.12 0.88
## 6 0.07 0.40 0.19 0.82 0.07
## 7 0.19 0.50 0.29 0.69 0.19 0.12
## 8 0.11 0.42 0.21 0.81 0.11 0.06 0.13
## 9 0.22 0.54 0.32 0.67 0.22 0.15 0.04 0.15
## 10 0.37 0.69 0.47 0.59 0.37 0.31 0.21 0.27 0.18
## 11 0.23 0.56 0.35 0.76 0.23 0.17 0.15 0.16 0.13 0.21
## 12 0.37 0.69 0.47 0.59 0.37 0.31 0.21 0.27 0.18 0.00 0.21
## 13 0.27 0.54 0.37 0.89 0.27 0.23 0.28 0.21 0.27 0.31 0.16 0.31
## 14 0.17 0.49 0.28 0.72 0.17 0.11 0.04 0.13 0.07 0.24 0.14 0.24 0.27
## 15 0.23 0.49 0.29 0.67 0.23 0.19 0.12 0.21 0.15 0.30 0.27 0.30 0.39 0.13x <- dist(scale(df[, c("fixed acidity", "alcohol", "volatile acidity")],
center = FALSE))
Y<- as.dist(round(as.matrix(x), 2)[1:1200, 1:1200])x <- df[, c("fixed acidity", "alcohol", "volatile acidity")]
x## # A tibble: 1,599 × 3
## `fixed acidity` alcohol `volatile acidity`
## <dbl> <dbl> <dbl>
## 1 7.4 9.4 0.7
## 2 7.8 9.8 0.88
## 3 7.8 9.8 0.76
## 4 11.2 9.8 0.28
## 5 7.4 9.4 0.7
## 6 7.4 9.4 0.66
## 7 7.9 9.4 0.6
## 8 7.3 10 0.65
## 9 7.8 9.5 0.58
## 10 7.5 10.5 0.5
## # ℹ 1,589 more rowsmultivariate outlier detection analysis using Mahalanobis distance. Let’s break down each part of the code:
Col Means: calculate the mean value of each column
cm <- colMeans(x)
cm## fixed acidity alcohol volatile acidity
## 8.3196373 10.4229831 0.5278205Calaculates the covariance of each column and the covariance matrix provides information about how the variables in the dataframe vary together.
Calculation of Mahalanobis disctance for each distance
MARGIN = 1 specifies that the function should be applied to each row
function(x): takes a row vector x as input and calculates the Mahalanobis distance for that row.
t(x - cm) %% solve(S) %% (x - cm): calculates the squared Mahalanobis distance for the row x
x - cm: This subtracts the column means (cm) from the row vector x
solve(S): This computes the inverse of the covariance matrix S
Plotting 3 clusters to identify outlier points for 3 attributes,
Center cluster is density based and indicating similarity
Outer clusters have varying nature and based on distance from the opposite end, they would display difference in nature.
Outermost cluster is not visible w.r.t colours but dark points at outer end are outliers
library(reshape2)## Warning: package 'reshape2' was built under R version 4.3.2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsS <- cov(x)
print(S)## fixed acidity alcohol volatile acidity
## fixed acidity 3.03141639 -0.11442115 -0.07985142
## alcohol -0.11442115 1.13564740 -0.03860022
## volatile acidity -0.07985142 -0.03860022 0.03206238d <- apply(x, MARGIN = 1, function(x)t(x - cm) %*% solve(S) %*% (x - cm))
print(d)## [1] 1.68615055 3.94121333 1.78965423 4.25204675 1.68615055 1.43555681
## [7] 1.03043999 0.74711321 0.87493802 0.30358052 2.37292881 0.30358052
## [13] 2.78787164 1.69523397 1.52712640 1.52712640 2.04286045 1.15527830
## [19] 2.16583191 3.87448971 4.81269207 1.86395078 1.56058738 1.09805120
## [25] 2.51877859 4.56151408 2.00616244 1.56058738 1.93214393 0.67838160
## [31] 1.28314747 1.22200106 0.72241548 1.68928611 8.84927618 0.90151311
## [37] 0.38250883 1.63470977 11.75293976 0.71935834 0.71935834 1.25842036
## [43] 0.34066743 0.55098442 1.76493963 10.25709949 5.68690316 3.17783956
## [49] 4.49621475 8.01716848 1.71466144 2.32674260 2.47185788 2.04236316
## [55] 0.30050773 0.97757509 1.92610956 1.26494999 0.29872477 2.79881707
## [61] 2.25400542 1.66647208 1.17228569 1.74164746 1.76737442 1.76737442
## [67] 0.99627239 1.70161641 1.42614364 1.06781408 1.00997002 1.20990223
## [73] 1.35207984 1.68725510 2.55828354 0.44771108 0.44771108 2.58887833
## [79] 1.95793526 1.62991467 4.14264096 1.83987554 2.27421520 1.54354433
## [85] 4.30535123 0.81989011 0.34344345 1.01564176 1.74625105 2.49215541
## [91] 0.93749833 0.34344345 0.45609605 1.01564176 9.34912604 9.30286426
## [97] 2.43394097 2.42740763 4.35911970 1.88651899 0.23405335 0.15594754
## [103] 1.88651899 1.35649517 1.81550390 1.35649517 2.30238993 2.76144311
## [109] 2.62514617 2.69518180 0.88363420 0.52164459 0.43512167 2.76394428
## [115] 0.88363420 0.71495659 0.15936921 1.06377190 0.39181259 2.23493336
## [121] 9.72487437 0.39181259 2.05982908 1.48974331 1.07856877 1.22145449
## [127] 23.64782705 23.49687996 0.14833261 4.85403037 2.00549258 7.85419042
## [133] 7.85419042 1.73450755 8.46348545 1.85473927 1.57439097 3.08312389
## [139] 0.88363420 0.88363420 1.85473927 1.57439097 14.50465548 4.29798044
## [145] 14.50465548 1.29699305 2.48825417 1.68545444 0.98891642 0.58844593
## [151] 2.23685249 1.13713217 0.38198684 0.38198684 1.07517239 1.07517239
## [157] 1.07517239 1.07517239 1.90750589 1.99773247 5.67625923 1.81999018
## [163] 0.28939050 1.27072371 1.33937368 0.97631442 0.96576992 2.35103580
## [169] 0.89075317 1.53672016 4.74279903 2.30138232 2.30138232 1.63671704
## [175] 2.71201676 1.86761310 2.71201676 0.25051243 2.80372144 0.93309012
## [181] 0.93309012 1.29689434 2.20658768 1.99332360 2.10596343 2.37931942
## [187] 3.20353175 1.24507265 1.02671581 1.08187743 1.10426443 3.56866711
## [193] 1.63523812 0.68912393 0.68912393 1.05351706 0.41206601 4.18051720
## [199] 11.81560141 12.40070670 1.64575408 2.36955288 1.99110333 3.26825643
## [205] 3.13852695 7.16862171 7.16862171 1.24949847 1.73796376 3.19649438
## [211] 4.71850411 1.25083649 3.55934566 0.12513490 1.46892666 0.75535517
## [217] 0.94177945 2.29601552 0.99115176 0.94944286 3.07366782 1.39852230
## [223] 1.61259886 0.59722513 0.41706320 0.57178995 0.89789975 3.38835238
## [229] 0.57178995 0.93071422 5.81128249 1.37539017 1.77873852 0.93071422
## [235] 7.90860519 2.32366704 2.32366704 1.88809807 2.32366704 7.90860519
## [241] 1.41907116 4.83157638 0.50108839 16.66372980 16.66372980 0.87378233
## [247] 1.57131968 1.15511170 0.54653033 0.87378233 2.96611567 0.78975842
## [253] 2.91643494 2.27028271 0.78975842 1.17472877 3.06780587 2.63690530
## [259] 2.14276407 1.94633508 3.41643249 6.48467133 0.23756802 2.76703903
## [265] 7.08616005 5.13677111 3.81816360 5.35219707 1.83706210 5.96615231
## [271] 0.12709220 5.96615231 2.58026081 1.73201743 1.33786657 0.12709220
## [277] 1.83706210 5.96615231 5.96215370 0.53687858 5.78636640 3.43598600
## [283] 1.98765921 0.53687858 1.34722960 1.34722960 4.48212261 1.08845631
## [289] 0.08310000 4.75220196 0.08310000 4.60820132 1.62911590 2.47829139
## [295] 8.90607077 2.63920163 6.09956647 1.20925504 1.07766851 1.52910859
## [301] 0.27891485 5.40144218 1.07149397 1.33044115 1.53953198 2.27374058
## [307] 1.18772582 2.25970253 2.13749148 4.19825386 2.27374058 1.08995873
## [313] 1.10615978 1.20230129 1.58077363 2.01534136 0.71699702 3.23723282
## [319] 3.45098444 3.23723282 3.45098444 0.97398516 1.28982051 2.17287909
## [325] 2.16606539 2.16606539 5.44434703 2.46788731 9.48837725 2.52766894
## [331] 5.29891733 5.29891733 0.98180183 0.40840148 2.33679104 11.33384311
## [337] 4.00618199 0.58655026 7.29653599 6.79916241 6.54285161 2.54060189
## [343] 2.72037769 2.72037769 5.18785949 1.22578929 7.22667485 13.76305590
## [349] 0.76042588 3.11968868 3.29081735 3.29288936 1.50608616 17.68402802
## [355] 7.05743841 8.07077815 3.76860121 3.20148832 4.23984797 6.32734321
## [361] 1.54372956 0.53841356 4.95164295 5.82452571 8.02823813 2.76939996
## [367] 8.02823813 3.12247473 3.11149955 3.71332048 2.08042038 7.36152249
## [373] 1.93599358 1.75925272 11.05413563 6.11993274 3.80122971 3.71332048
## [379] 13.97602826 0.43938819 4.22011493 9.60741718 4.22011493 4.22011493
## [385] 1.72483286 0.87468490 1.53230199 0.96129895 2.02316821 1.24005727
## [391] 11.43887398 9.60741718 2.03140863 1.83180718 7.51263901 11.04183160
## [397] 1.97298885 4.80187098 4.80187098 2.49967737 1.97298885 2.82314448
## [403] 5.14910987 4.06847986 2.05538519 1.82137455 0.92664469 4.72038454
## [409] 2.57170941 6.16707342 1.31279524 1.19236053 2.31205841 2.45138423
## [415] 0.82830483 2.06174672 3.49462393 0.58705355 4.32562024 2.20939396
## [421] 2.71564236 7.79132003 6.88980927 3.44716992 6.88980927 7.79132003
## [427] 2.48748418 3.49040315 1.28445576 13.31452402 3.44716992 1.50260234
## [433] 9.83643107 5.88677990 1.82389353 5.88677990 0.63316865 3.26496331
## [439] 1.82389353 1.79741427 6.75600117 5.13474326 23.74851547 2.29168270
## [445] 6.45681257 2.35721534 5.84322901 0.90498064 0.06591459 4.48198083
## [451] 4.48198083 3.00972106 0.98863928 2.51239011 4.29196014 14.21097635
## [457] 0.31146142 1.13332276 2.51239011 5.33167415 1.04759692 1.18857157
## [463] 5.01909877 2.01796367 5.42389317 2.98490263 2.62284900 11.70978669
## [469] 4.78357054 3.12925378 7.51677636 1.71604133 5.84511167 1.44510781
## [475] 4.13102301 1.70089659 3.06049479 3.34271519 1.70089659 1.53586484
## [481] 4.71181824 2.72090234 3.20274086 3.20274086 10.26240954 2.39999382
## [487] 2.39999382 2.10756915 4.14457621 0.84439435 3.13263774 7.74745404
## [493] 8.06082024 2.94384303 3.94243470 2.55088926 2.12419644 2.09308641
## [499] 2.55088926 2.94384303 2.12419644 3.87766973 3.87766973 3.11212558
## [505] 3.35800632 5.53415988 3.25632132 4.56749370 1.30976284 9.52508055
## [511] 6.04376383 1.30976284 2.49839746 3.75677772 3.75677772 1.40244234
## [517] 10.19449980 4.92041298 5.26329453 1.61435091 3.08171860 3.00484208
## [523] 1.29948592 1.84881861 1.36087058 2.40042759 1.61435091 2.10983935
## [529] 3.71386566 2.05730889 3.18134174 4.32788721 4.32788721 7.07611258
## [535] 1.05563475 3.18134174 2.05730889 3.27265190 9.62928399 3.65899584
## [541] 0.91855213 0.80786791 1.86513635 5.06669021 15.21518952 1.93309144
## [547] 1.06781361 2.56536121 5.65844811 1.04152691 1.92151758 0.75894691
## [553] 0.67642645 11.87087600 21.89714020 21.89714020 4.37866048 22.44733943
## [559] 4.37866048 13.40089849 9.33715543 1.87649138 1.42665844 2.83703072
## [565] 13.40089849 9.33715543 2.39961806 2.39961806 0.84016721 3.88356375
## [571] 3.93071595 3.88356375 3.13629627 2.35328589 2.50660886 6.08323681
## [577] 0.94058055 1.21355936 0.55936775 3.07849690 5.72256059 5.72256059
## [583] 4.83427374 5.55631668 4.43528700 0.97257068 3.25398620 2.15385225
## [589] 14.33063428 2.36176429 0.83992314 2.62963259 0.83992314 2.54233755
## [595] 1.59307852 1.24469400 6.43241777 4.95164295 0.40056327 7.85287996
## [601] 4.93782308 9.22154889 3.46380501 9.22154889 1.54927697 1.34877503
## [607] 3.32362899 0.11939652 2.07236089 6.31688759 4.94975343 8.56726263
## [613] 0.59580518 1.29948592 2.37128321 1.17440628 1.17440628 4.37876931
## [619] 3.14327018 4.31000890 0.95024995 0.94062346 1.73234419 2.53380690
## [625] 1.27123458 1.27123458 1.62883891 1.62883891 1.14624902 1.66373663
## [631] 1.14624902 3.82433270 0.81357858 10.23844672 2.59284132 3.57635738
## [637] 5.69089590 5.67125802 4.78472800 1.92079566 1.65604498 1.27239000
## [643] 1.65604498 1.27239000 1.65604498 0.45231881 0.82460633 4.30900914
## [649] 0.60395543 1.96427671 2.47982290 7.59481756 40.51746272 3.12297494
## [655] 1.00995220 1.48007059 2.47982290 4.71837277 1.24342746 3.89700154
## [661] 1.24342746 3.26672757 1.08815983 2.56929360 4.75427299 0.94752338
## [667] 0.93829948 4.81337392 3.85835580 4.81337392 2.04032190 1.72176000
## [673] 19.58637626 1.72176000 3.32028054 0.69125460 3.32028054 2.64904825
## [679] 2.11926864 4.34941996 9.53560211 0.39924178 0.60488988 3.40837967
## [685] 8.85124454 3.40837967 2.20277427 1.03045968 1.30501075 2.24651023
## [691] 14.82330301 6.01475122 2.02800468 1.18780089 1.23816144 8.04146580
## [697] 1.50472723 1.50472723 1.08478772 4.27356541 12.15704412 1.50472723
## [703] 1.64243187 2.28477417 2.52303066 8.69280100 2.17948748 0.40927753
## [709] 1.34779662 2.08911722 12.34911798 0.84193043 1.58897518 1.71325170
## [715] 3.01550146 2.28451765 1.71325170 0.72555969 0.59700479 1.46662281
## [721] 0.59700479 1.59324345 1.07263567 4.18975192 13.08468635 0.94628630
## [727] 1.97716091 2.16271767 2.16271767 10.26759398 1.01657177 6.88835425
## [733] 3.17348203 2.18523556 1.31262110 6.16069526 6.16069526 1.13924388
## [739] 1.73024279 1.90388019 3.61220330 0.95733653 2.56938357 3.69214940
## [745] 3.45688578 1.38545797 3.89126686 2.75529796 1.35478126 1.38545797
## [751] 1.02479820 1.02479820 1.21814470 1.02479820 2.11497099 5.17269318
## [757] 8.12609167 3.81816360 3.81816360 2.04367142 0.65199367 1.40477812
## [763] 1.23688536 1.40477812 1.59475398 1.57431087 1.01908338 1.03439847
## [769] 1.01354818 1.47436088 1.01354818 2.02405604 1.35469392 1.89491823
## [775] 1.89491823 6.97572666 2.00660073 0.83328612 0.32630170 1.03292508
## [781] 1.62786734 2.21638637 3.36355183 2.21638637 0.88737054 2.47490918
## [787] 2.47490918 1.14841312 1.14841312 1.28548230 2.33647336 0.96769669
## [793] 1.03010280 0.40401377 5.72295782 8.33109590 0.89529523 0.94817983
## [799] 0.47964469 0.47964469 1.42296564 0.18355027 8.44631734 0.76805343
## [805] 0.26278113 5.92275413 5.22219725 5.92275413 1.84129410 0.37463806
## [811] 0.48505135 7.77348162 2.22608777 2.12037421 7.03592546 2.22608777
## [817] 0.76242476 4.67288888 1.97197415 1.29439685 1.41311568 14.54352334
## [823] 1.38796329 1.38796329 0.77711005 0.83204798 2.85076802 0.83204798
## [829] 5.03092732 2.29077749 1.21018812 2.29077749 1.68803612 3.69739224
## [835] 1.58965871 1.54284722 4.29666829 4.29666829 2.36375998 2.41341221
## [841] 5.39060629 1.23167013 2.09431022 1.75937502 2.69376796 1.68877253
## [847] 1.68877253 0.95996832 1.68877253 1.66694001 1.39565384 1.39565384
## [853] 1.23339571 1.08354828 1.08354828 3.48099457 1.08354828 2.68486663
## [859] 4.61755101 1.23177354 1.26306325 10.13082332 0.83148231 0.97311390
## [865] 1.26306325 1.01747655 1.43567367 1.15621287 1.23177354 0.69986386
## [871] 3.45885116 0.67831086 2.25941479 3.23073647 3.35518066 2.62920981
## [877] 0.84799935 3.45885116 0.44579333 1.23888767 0.51676740 2.42022304
## [883] 3.08240035 1.23888767 0.44579333 2.16462439 3.11511647 5.13401719
## [889] 1.24839208 8.35331445 2.40439847 1.12659055 2.20207444 1.12659055
## [895] 0.88648865 0.59013156 4.51956652 0.59013156 4.51956652 9.54590428
## [901] 2.64377976 0.70368713 0.70368713 0.89255294 0.89255294 1.01939138
## [907] 0.66649573 2.50093642 0.39587265 2.86949598 7.32855351 1.95879250
## [913] 3.99401516 3.35528497 2.86949598 2.78103649 3.74415713 1.92837498
## [919] 1.03267270 2.42577039 0.77689762 1.03267270 2.42577039 1.92837498
## [925] 0.59044096 3.06577274 2.90451168 1.66535366 0.59044096 2.87399306
## [931] 1.02464386 0.70143673 2.13049386 0.70143673 1.02464386 2.74096385
## [937] 2.74096385 5.93525611 5.92430806 2.48244188 4.44899085 5.52527092
## [943] 1.30163040 1.12257396 3.46110118 1.66500146 3.58452010 4.53761870
## [949] 6.10142536 6.10142536 6.10142536 4.53761870 1.83647268 4.05974143
## [955] 2.97400573 3.47179279 1.35198549 1.27290321 1.80360171 0.25114657
## [961] 0.45147797 1.76354712 1.89290305 2.27493420 0.45147797 1.27635035
## [967] 2.34455941 1.61711367 3.09201366 0.95211428 3.00515401 3.00515401
## [973] 2.52099943 1.13022137 3.14664766 2.64001608 2.64001608 1.55086868
## [979] 1.79364477 5.00998511 0.21129963 4.87933484 5.70559090 0.21129963
## [985] 5.00998511 1.02829971 2.09804249 3.51467767 2.39973609 1.97801980
## [991] 2.39973609 3.86541242 3.78542408 3.86541242 2.74824646 0.29809770
## [997] 7.74362702 7.74362702 4.35752335 7.73425132 1.41927471 1.44510781
## [1003] 2.71675943 6.04605348 0.41181253 6.04605348 2.71675943 2.60123489
## [1009] 2.74930373 0.80712031 4.22940881 1.41559490 7.25097944 1.13206488
## [1015] 0.73276295 2.62047672 4.47891397 7.18845684 7.18845684 0.72895527
## [1021] 5.19689363 5.19689363 0.66870806 3.81420423 0.17954053 3.38708721
## [1027] 4.97151102 4.62178858 2.40439847 0.17954053 1.59277395 0.60736345
## [1033] 2.92872617 0.23636139 2.04815811 1.46387797 4.80103341 5.10646019
## [1039] 4.31841259 0.54900377 6.06413425 1.42191615 0.54900377 0.96964535
## [1045] 4.33953012 1.71145554 2.29313060 1.11234348 0.55227635 0.47472460
## [1051] 1.11234348 1.72692982 7.72653818 5.04212268 1.68987808 1.68987808
## [1057] 2.69396515 2.87719466 2.45619838 2.69396515 5.48875089 4.15421878
## [1063] 1.36960088 3.66100784 4.03883129 0.29377702 4.56946803 3.38316717
## [1069] 3.38316717 0.48366245 1.51476344 2.68211417 3.31108982 0.40331071
## [1075] 2.68211417 2.69276043 2.35067838 0.91487052 0.91487052 4.14150840
## [1081] 3.97212701 4.14150840 2.83026231 2.34045040 2.83026231 1.95532199
## [1087] 1.93426522 4.19200925 3.76532840 3.76532840 3.78892681 1.81526428
## [1093] 1.51526339 3.19246288 2.00036515 0.71495659 2.00036515 0.98183400
## [1099] 4.59721282 0.98183400 3.91574889 2.38255905 2.27962349 2.38255905
## [1105] 3.45558491 6.21526977 4.99883687 2.60123489 2.36553511 2.24695034
## [1111] 0.16564736 6.07557197 1.58279617 4.11562785 12.24042060 2.05573396
## [1117] 2.05573396 2.05573396 7.72717514 8.19232787 6.66272267 3.87952198
## [1123] 4.28248435 2.57220527 2.05107704 2.84855056 11.28467817 3.86888396
## [1129] 1.20261301 1.63260623 0.50498672 9.61581327 9.21249759 0.97967948
## [1135] 3.00240785 3.82977338 2.67150224 2.67150224 3.10128334 0.87186551
## [1141] 2.43054304 0.88811803 1.65077756 5.33019371 2.62150166 3.88226966
## [1147] 0.60335640 2.78035601 1.69058491 3.49868375 5.43484891 5.29508191
## [1153] 0.42749288 1.08490713 1.76837577 0.42749288 4.70611755 8.33280375
## [1159] 1.73569551 1.43819745 2.45344849 0.29914035 2.51791096 2.67905417
## [1165] 2.67905417 1.43380150 0.92784146 4.02391305 4.24636537 1.14742594
## [1171] 0.96154538 0.65025610 3.91805514 2.69817206 2.69817206 2.16998254
## [1177] 5.37347875 6.05686204 7.36840320 1.19396846 1.19396846 1.79611622
## [1183] 1.37239523 2.07680801 1.27126384 2.60053197 6.52199596 2.60053197
## [1189] 1.27126384 6.56840249 4.15421878 4.79162100 5.68388289 4.85065510
## [1195] 2.16730019 3.64850604 0.83869189 0.49610627 2.82481065 0.83869189
## [1201] 0.49610627 1.59731556 1.59046755 3.32162007 1.77635854 1.77635854
## [1207] 1.77635854 2.64997771 1.77635854 2.90453156 0.98811402 3.32248971
## [1213] 0.98811402 0.97631215 1.92361869 2.36894253 0.83757150 2.81388290
## [1219] 0.57737511 0.73780572 3.74415967 3.74415967 0.66781654 4.08762247
## [1225] 6.06486849 1.35256797 1.61989940 0.77307695 11.89828437 1.77860534
## [1231] 5.22227026 3.09211382 1.77860534 5.04177123 7.96903181 4.38223461
## [1237] 1.08126068 7.96903181 1.59389206 3.31831473 1.41015786 1.69356254
## [1243] 3.09201366 0.75453229 5.34015779 0.28057966 1.49418230 0.28057966
## [1249] 1.62392509 0.76482427 0.76482427 0.60109011 1.53163748 1.10092396
## [1255] 1.35517805 2.02190119 1.61248819 0.86781286 1.72376897 1.72376897
## [1261] 1.31050901 9.37768229 1.13250970 2.29076104 2.07058299 0.43920407
## [1267] 0.43920407 2.41313371 2.79291340 13.27794378 14.57911317 1.86691749
## [1273] 2.74465343 1.40352231 0.75100616 1.53964628 2.37579261 1.44705812
## [1279] 1.53964628 2.61909883 0.86438032 0.86438032 2.32473726 0.47051314
## [1285] 1.30195754 3.14600967 7.59218977 6.72830965 0.66662110 0.66662110
## [1291] 2.04072456 0.72055978 5.64346715 1.83676763 0.72055978 1.85298759
## [1297] 1.85298759 2.81362217 4.90120808 39.11458009 7.00681750 6.10188171
## [1303] 1.18304278 2.38117596 2.92074250 0.64654283 1.94431052 1.74174142
## [1309] 1.94431052 2.01036443 0.64654283 5.81503162 14.72381648 2.34296213
## [1315] 2.34296213 0.71202089 7.74758002 2.91487747 0.71202089 3.16038227
## [1321] 1.57796522 8.36088159 1.69298448 1.07524384 1.27410837 1.27410837
## [1327] 1.27410837 1.27410837 2.69632954 0.68120142 0.68120142 4.26682550
## [1333] 1.38557463 2.75916038 2.99327372 5.17025739 3.23691936 3.23691936
## [1339] 3.23691936 0.27352885 0.27352885 0.27352885 0.17567411 0.27352885
## [1345] 3.76620546 0.39760469 2.37753275 1.37308479 1.37308479 0.76708155
## [1351] 0.49266851 2.42784336 0.49304621 0.49304621 0.60595013 4.55400563
## [1357] 4.17234217 1.06354969 1.18749679 3.63215892 0.58889652 3.46784505
## [1363] 3.63215892 2.96213677 2.31779397 1.07856877 1.64139996 0.68617624
## [1369] 1.94737000 1.02215867 3.00668058 1.56878930 3.00668058 2.23079717
## [1375] 2.90650044 0.70747422 4.19215021 5.76628350 1.27321623 0.36653354
## [1381] 0.36653354 3.30390050 0.35509299 0.35509299 2.21456374 3.21588567
## [1387] 1.04115236 1.04115236 1.13321780 1.90967955 5.29680165 0.86142112
## [1393] 0.93051878 0.10432243 2.80942219 1.30408903 1.25443786 0.62918300
## [1399] 0.66662110 1.42063358 0.92119965 0.92119965 3.34524009 2.70643840
## [1405] 0.12044558 2.67622578 2.83508921 1.98883252 4.47274037 1.98883252
## [1411] 9.51185495 1.60969814 2.83508921 1.07844256 2.73135687 2.62626295
## [1417] 2.73135687 3.55475359 0.49752079 0.59242211 0.49752079 2.44904378
## [1423] 1.33905125 1.47192794 3.73704972 3.73704972 4.80094604 1.08726785
## [1429] 0.86179355 4.47952510 1.01304082 1.09871167 3.58384517 3.19619782
## [1435] 2.78715642 2.78715642 5.02814881 5.91534834 0.61646180 1.33255473
## [1441] 1.89219515 2.39034104 1.12542995 2.11359775 1.38112055 2.35613734
## [1447] 1.12542995 1.48978950 1.20455993 1.78553516 1.89219515 1.78493524
## [1453] 2.14905386 2.45619269 4.13198143 5.28851749 1.85563118 2.45619269
## [1459] 1.91353168 4.80502674 2.14564750 2.88173653 0.93591597 0.96413070
## [1465] 1.30154549 1.30154549 0.82668897 9.49450615 0.82668897 6.40828310
## [1471] 2.48347422 6.33491430 1.59536617 1.87597789 3.05135122 11.14511391
## [1477] 3.05135122 11.32244706 3.83031484 2.13604925 2.60089934 2.13604925
## [1483] 0.44737097 1.39983745 5.41538461 1.24639606 1.18218031 1.88603551
## [1489] 3.14190032 1.56033716 6.33040750 3.14190032 3.83580838 0.65417896
## [1495] 4.32214109 1.16421677 0.65417896 3.17803330 4.97076185 3.17803330
## [1501] 1.59737793 3.07843639 0.73097432 2.25335619 1.65017352 2.13254359
## [1507] 3.11990275 1.65017352 3.36564979 4.38164678 3.93217768 1.27743174
## [1513] 2.74703225 1.22729964 3.87658310 3.85009049 4.06342633 1.22280036
## [1519] 0.50845882 1.52448272 1.22280036 3.13056935 4.06342633 1.05754112
## [1525] 3.24470933 1.90967955 2.04342567 0.59864271 2.56200447 1.06253580
## [1531] 1.93343124 2.11438116 0.77734449 3.24738970 2.30899120 1.16150074
## [1537] 1.80663745 2.95821817 3.89961437 1.94060053 2.56493537 3.32467276
## [1543] 3.97445947 2.56074528 1.10742760 1.28368513 0.64048319 3.82385899
## [1549] 2.82708418 1.93515542 1.57131968 1.67681943 2.49262657 2.04096914
## [1555] 4.38837357 0.57973628 4.14335038 4.38837357 1.53710897 0.40153922
## [1561] 0.40153922 0.40153922 1.07451852 1.07451852 1.07451852 1.48192836
## [1567] 6.50937721 1.07451852 2.09225153 2.40884692 5.19581809 2.63012869
## [1573] 1.51151903 5.41872927 5.40704053 1.83742477 1.89782547 4.19684129
## [1579] 1.97133978 2.04471495 2.89861448 2.04471495 4.48021820 2.70525944
## [1585] 3.50897261 1.94760961 2.48545109 2.25454283 6.48262067 2.82582052
## [1591] 2.41977591 4.88925317 1.66455125 1.62040087 1.49138011 2.35629772
## [1597] 1.66455125 2.08034892 4.38149443# Perform k-means clustering
k <- 3 # Assuming you want to cluster into 3 groups
cluster <- kmeans(d, centers = k)
# Plot clusters
plot(x, col = cluster$cluster, main = "Cluster Plot")
# Add cluster centers
points(cluster$centers, col = 1:k, pch = 8, cex = 2)
QQ plot:
This function generates a QQ plot of the distribution of the variable alcohol against the theoretical normal distribution. In a QQ plot, if the points fall approximately along a straight line, it suggests that the data is approximately normally distributed.
QQline: adds a line through the first and third quartiles of the data.
Inference:
Alcohol attribute seems to be normally distributed at middle of Q-Q Quantile line and deviating at the edges of Sample and Theorotical Quantiles
qqnorm(df$alcohol, main = "alcohol")
qqline(df$alcohol)
Inference:
fixed acidity shows high deviation at the edges of Sample and Theorotical Quantiles comparatively indicating a varying nature in the attribute
qqnorm(df$`fixed acidity`, main = "fixed acidity")
qqline(df$`fixed acidity`)
Inference:
pH seems to be evenly distibuted indiacinting a bad parameter to use for future analysis. As we cannot differnetiate if wine is good or bad based on pH values
qqnorm(df$pH, main = "pH")
qqline(df$pH)
plot to assess the distribution of distances resulting from a clustering operation
sort(d): Sorts the distances in ascending order.
qchisq((1:nrow(x) - 1/2) / nrow(x), df = 3): Generates quantiles from a chi-squared distribution with 3 degrees of freedom.
As the distance of data points increase as per the sorted order, it indicates decreasing normality
plot(qchisq((1:nrow(x) - 1/2) / nrow(x), df = 3), sort(d),
xlab = expression(paste(chi[3]^2, " Quantile")),
ylab = "Ordered distances")
abline(a = 0, b = 1)
df$quality <- as.factor(df$quality)
df_new <- df[, 1:11] #selecting first 11 columns
df_new## # A tibble: 1,599 × 11
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 6 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>Calculation of Mahalanobis disctance for each distance MARGIN = 1 specifies that the function should be applied to each row
function(x): takes a row vector x as input and calculates the Mahalanobis distance for that row.
t(x - cm) %% solve(S) %% (x - cm): calculates the squared Mahalanobis distance for the row x
x - cm: This subtracts the column means (cm) from the row vector x solve(S): This computes the inverse of the covariance matrix S
df_cm <- colMeans(df_new)
df_S <- cov(df_new)
df_d <- apply(df_new, MARGIN = 1, function(df_new)t(df_new - df_cm) %*% solve(df_S) %*% (df_new - df_cm))
df_cm #means of all the column## fixed acidity volatile acidity citric acid
## 8.31963727 0.52782051 0.27097561
## residual sugar chlorides free sulfur dioxide
## 2.53880550 0.08746654 15.87492183
## total sulfur dioxide density pH
## 46.46779237 0.99674668 3.31111320
## sulphates alcohol
## 0.65814884 10.42298311df_S # covarience against each other## fixed acidity volatile acidity citric acid
## fixed acidity 3.031416389 -0.079851416835 0.2278200037
## volatile acidity -0.079851417 0.032062377652 -0.0192716208
## citric acid 0.227820004 -0.019271620776 0.0379474831
## residual sugar 0.281756262 0.000484190976 0.0394342700
## chlorides 0.007678692 0.000516586920 0.0018687248
## free sulfur dioxide -2.800921493 -0.019673590385 -0.1242521139
## total sulfur dioxide -6.482345858 0.450425692372 0.2276972740
## density 0.002195224 0.000007443665 0.0001341746
## pH -0.183585704 0.006494699304 -0.0162975823
## sulphates 0.054010092 -0.007921433844 0.0103277145
## alcohol -0.114421153 -0.038600221431 0.0228151729
## residual sugar chlorides free sulfur dioxide
## fixed acidity 0.2817562623 0.00767869249 -2.8009214927
## volatile acidity 0.0004841910 0.00051658692 -0.0196735904
## citric acid 0.0394342700 0.00186872478 -0.1242521139
## residual sugar 1.9878971330 0.00369017590 2.7586114522
## chlorides 0.0036901759 0.00221514265 0.0027383031
## free sulfur dioxide 2.7586114522 0.00273830308 109.4148838331
## total sulfur dioxide 9.4164414790 0.07338675025 229.7375209475
## density 0.0009454109 0.00001782176 -0.0004332504
## pH -0.0186442890 -0.00192574496 0.1136530908
## sulphates 0.0013209414 0.00296187795 0.0915924710
## alcohol 0.0632189598 -0.01109151777 -0.7736984004
## total sulfur dioxide density pH
## fixed acidity -6.482345858 0.002195223576 -0.18358570360
## volatile acidity 0.450425692 0.000007443665 0.00649469930
## citric acid 0.227697274 0.000134174581 -0.01629758234
## residual sugar 9.416441479 0.000945410862 -0.01864428898
## chlorides 0.073386750 0.000017821757 -0.00192574496
## free sulfur dioxide 229.737520947 -0.000433250416 0.11365309083
## total sulfur dioxide 1082.102372533 0.004424727145 -0.33769879250
## density 0.004424727 0.000003562029 -0.00009956395
## pH -0.337698793 -0.000099563948 0.02383518055
## sulphates 0.239471005 0.000047509618 -0.00514618582
## alcohol -7.209297895 -0.000997951790 0.03383161664
## sulphates alcohol
## fixed acidity 0.05401009157 -0.1144211534
## volatile acidity -0.00792143384 -0.0386002214
## citric acid 0.01032771452 0.0228151729
## residual sugar 0.00132094136 0.0632189598
## chlorides 0.00296187795 -0.0110915178
## free sulfur dioxide 0.09159247097 -0.7736984004
## total sulfur dioxide 0.23947100464 -7.2092978950
## density 0.00004750962 -0.0009979518
## pH -0.00514618582 0.0338316166
## sulphates 0.02873261613 0.0169067772
## alcohol 0.01690677723 1.1356473950df_d #mahanlobis distance obtained## [1] 4.920027 8.335593 3.647326 7.439998 4.920027 5.051613
## [7] 4.154062 6.280961 3.930883 12.973615 4.276886 12.973615
## [13] 8.159632 44.766955 20.060139 19.797004 9.091949 43.248906
## [19] 8.793605 34.245925 13.325602 6.976487 6.972829 8.057862
## [25] 4.019224 6.627345 4.699302 6.972829 3.511154 4.130431
## [31] 3.701115 4.678032 11.411715 42.735827 12.726209 11.589660
## [37] 4.805375 3.707565 26.689837 13.405664 13.405664 2.652565
## [43] 36.013859 19.803121 5.279714 26.600536 19.394203 6.466224
## [49] 8.630697 17.091340 7.073834 5.800559 6.436202 7.749945
## [55] 6.743370 5.117256 11.732185 15.436803 1.522250 6.381721
## [61] 7.364595 13.034591 1.826630 2.418405 8.851193 8.851193
## [67] 2.839662 5.205228 5.477813 9.552324 2.631209 6.304982
## [73] 5.879607 2.899907 6.313075 15.410934 15.410934 4.848264
## [79] 5.134748 19.882636 7.875328 69.972312 8.669783 48.197270
## [85] 10.862690 1.386868 91.662510 3.054152 18.994297 5.521029
## [91] 11.374174 91.662510 94.850656 3.054152 28.754749 27.891506
## [97] 4.653863 7.415801 6.975461 2.689589 1.969879 1.941679
## [103] 2.689589 3.643194 4.970581 3.643194 70.750899 9.755047
## [109] 7.823618 20.959016 6.708068 14.290683 13.149582 5.346942
## [115] 6.708068 5.557272 2.002914 3.179724 10.140829 4.811377
## [121] 18.897742 10.140829 6.550495 4.686116 10.055811 12.767985
## [127] 28.671033 28.583424 8.494575 7.110155 18.612302 18.378206
## [133] 18.378206 4.356204 12.874290 6.840085 4.657808 7.164892
## [139] 9.190875 8.681792 6.840085 4.657808 21.898140 8.112881
## [145] 21.898140 17.356111 9.340947 19.407117 2.018725 7.514434
## [151] 8.767746 155.546421 9.718788 9.718788 12.884164 12.872877
## [157] 12.884164 12.872877 4.556973 9.948619 10.501667 20.971848
## [163] 3.405232 17.509906 16.989577 10.992022 6.444330 6.860250
## [169] 3.040196 57.357244 9.197074 4.655417 4.655417 6.472516
## [175] 5.460506 4.886199 5.460506 9.251791 5.082773 3.646279
## [181] 3.646279 21.700762 3.205738 6.902345 7.138660 8.578443
## [187] 8.889035 2.959663 18.273319 17.618293 9.119993 8.965678
## [193] 12.981559 2.589320 2.589320 7.942365 2.782360 10.276858
## [199] 23.879863 15.386930 4.767929 18.367729 4.932907 6.305575
## [205] 6.419886 11.111126 11.111126 8.351116 5.108093 10.926016
## [211] 13.233222 3.308834 10.940222 4.550328 4.833248 15.265653
## [217] 6.595323 6.301627 3.861572 12.962639 5.098954 5.192780
## [223] 6.331448 3.647383 3.531300 2.568314 53.602057 5.018884
## [229] 2.568314 6.493461 15.353558 8.420229 4.192963 6.493461
## [235] 12.999580 4.015091 4.015091 3.368635 4.015091 12.999580
## [241] 21.936116 6.124572 13.073953 43.186942 43.186942 14.666662
## [247] 2.353224 6.068651 4.076844 14.666662 4.959878 4.197355
## [253] 5.589073 13.901212 4.197355 10.482013 5.089693 5.360756
## [259] 134.546439 6.770112 4.559680 10.178379 5.141549 4.324929
## [265] 10.667899 17.868485 14.762427 17.646695 18.310442 17.485417
## [271] 5.277759 17.485417 5.693168 3.094392 14.290062 5.277759
## [277] 18.310442 17.485417 15.563216 8.837489 14.231637 44.114247
## [283] 4.338673 8.837489 12.703942 12.703942 8.541270 4.859529
## [289] 5.923636 12.178045 5.923636 63.517238 5.752755 5.232773
## [295] 11.340046 9.340742 10.113672 11.412538 15.360818 11.268265
## [301] 3.172784 10.583813 7.275719 11.088512 15.216735 8.640993
## [307] 5.390703 13.703354 13.169599 7.838407 8.640993 8.911043
## [313] 6.167938 10.525111 3.883568 3.708515 9.423222 7.738353
## [319] 7.507990 7.738353 7.507990 6.223870 4.370161 8.559963
## [325] 47.639964 47.639964 9.408438 6.934051 15.213809 5.227148
## [331] 13.967973 13.967973 6.943571 9.858420 7.560182 13.731962
## [337] 6.024718 5.051349 18.640221 29.113820 22.286947 5.784144
## [343] 9.562695 9.562695 9.881441 15.149021 12.280488 16.838109
## [349] 5.677600 10.443481 11.162022 9.754813 6.179556 32.509979
## [355] 29.672837 12.474536 10.225598 6.771902 6.723533 8.867990
## [361] 4.745428 4.354240 9.885132 10.835550 15.221493 5.926847
## [367] 15.221493 12.565099 9.171524 12.487487 11.003146 14.875483
## [373] 10.603210 9.662062 13.866781 13.622147 13.631982 12.487487
## [379] 26.929117 5.800538 7.127566 11.054531 7.127566 7.127566
## [385] 4.402662 3.990146 4.508273 1.729694 4.690830 2.703677
## [391] 22.509328 11.054531 6.360153 16.867925 11.708200 26.829429
## [397] 41.656067 6.724541 6.724541 8.394025 41.656067 6.079496
## [403] 7.869859 5.033708 5.123809 5.768175 3.160080 6.879059
## [409] 10.294485 22.663817 5.120354 4.426230 4.437217 13.446272
## [415] 7.357214 24.820200 7.935281 10.657719 7.859731 11.901255
## [421] 9.280072 13.852993 11.111234 6.647124 11.111234 13.852993
## [427] 3.680045 8.766660 3.195401 18.546335 6.647124 4.974192
## [433] 13.939990 10.397004 7.014309 10.397004 11.934461 9.685295
## [439] 7.014309 12.418311 12.180293 8.710341 28.418984 3.684540
## [445] 11.285683 6.668591 9.937904 3.798134 2.666583 8.322098
## [451] 8.322098 52.579090 3.880796 11.955883 7.751545 22.255194
## [457] 5.208874 4.455552 11.955883 13.483985 5.901772 3.870816
## [463] 7.276874 28.149072 8.274249 14.245700 8.802393 13.544736
## [469] 9.695007 18.234639 9.101908 4.734403 9.557232 5.604789
## [475] 8.208444 8.239281 4.677328 9.519887 8.239281 6.157245
## [481] 110.262324 7.561378 10.372171 10.754696 20.701860 6.491046
## [487] 6.491046 7.034234 14.380913 2.821336 5.599733 8.853597
## [493] 11.048469 22.618208 24.993133 3.963836 4.651439 13.447201
## [499] 3.963836 22.618208 4.651439 22.226282 22.226282 8.808702
## [505] 10.385774 10.565586 9.234859 10.090655 8.590131 12.610891
## [511] 23.623586 8.590131 4.233940 10.629532 10.629532 27.801540
## [517] 15.357348 12.700369 7.283234 11.530814 11.350598 11.285742
## [523] 16.864850 12.523855 7.833912 7.681419 11.530814 10.677923
## [529] 13.801450 5.535649 15.546635 7.894996 7.894996 15.461077
## [535] 8.386108 15.546635 5.535649 7.877354 21.927314 13.384218
## [541] 2.024346 12.562800 6.078282 13.287724 20.235134 11.597548
## [547] 5.966517 5.332746 9.494580 8.306060 4.935417 2.888068
## [553] 4.073094 34.473043 28.851750 28.851750 22.335863 29.130642
## [559] 22.335863 33.312882 10.538656 8.867177 13.466724 7.658619
## [565] 33.312882 10.538656 14.193050 14.193050 18.297522 6.994749
## [571] 10.257754 6.994749 4.591244 6.267839 4.325665 13.304693
## [577] 6.601389 8.882896 8.576467 5.885657 10.446887 10.446887
## [583] 9.269771 9.810193 26.891111 4.120363 7.159775 6.767614
## [589] 18.040647 7.132700 5.499106 28.327992 5.499106 9.377321
## [595] 3.456157 18.277832 8.735346 10.433427 3.613963 9.756151
## [601] 10.609212 12.281014 6.393786 12.281014 5.935057 3.724775
## [607] 6.673666 5.526735 22.358397 14.727901 11.236321 10.962476
## [613] 3.569389 4.581328 44.027224 14.126020 14.126020 9.065482
## [619] 8.331089 7.371490 8.579558 7.662706 4.547680 10.319313
## [625] 16.183660 16.183660 5.428707 5.428707 2.280515 3.974002
## [631] 2.280515 6.928410 3.627401 26.525481 12.307757 13.713045
## [637] 18.002057 17.288323 7.233324 24.831749 6.851821 8.975394
## [643] 6.851821 8.975394 6.851821 11.883815 4.259341 7.021414
## [649] 1.091952 40.125152 14.297275 39.862879 73.611240 19.095733
## [655] 14.321382 13.063336 14.297275 13.450695 8.444632 8.635937
## [661] 8.444632 6.380303 4.497542 8.807664 8.635304 7.558835
## [667] 15.585407 9.461573 8.742363 9.461573 8.172810 6.478808
## [673] 37.192911 6.478808 5.182176 3.412355 5.182176 4.124989
## [679] 14.330239 11.409738 13.430135 4.346300 6.376852 7.049247
## [685] 23.777396 7.049247 4.159682 5.886207 6.255291 11.835684
## [691] 26.709848 16.115410 52.421001 10.144023 9.893119 24.899069
## [697] 4.405072 4.405072 6.844765 5.605351 20.620128 4.405072
## [703] 4.538459 6.834097 5.877495 16.179737 4.290022 2.191603
## [709] 4.074750 4.350638 20.144563 12.000251 7.552151 5.201072
## [715] 5.870543 4.554262 5.201072 6.313646 7.965535 4.720424
## [721] 7.965535 10.132713 7.398499 69.334726 19.669599 2.926000
## [727] 15.590036 6.887504 6.887504 13.795403 51.939373 16.467469
## [733] 4.789867 8.799426 5.712138 9.694288 9.694288 13.098429
## [739] 9.067645 6.687146 9.025310 12.262290 6.004500 11.680212
## [745] 10.975006 5.351662 6.826664 5.804612 4.916397 5.351662
## [751] 2.744415 2.744415 3.025941 2.744415 55.929356 11.811350
## [757] 10.712092 6.018306 6.018306 8.587790 10.586367 4.736538
## [763] 5.202461 4.736538 3.991566 4.472538 5.394058 13.431561
## [769] 9.233562 4.368379 9.233562 16.733672 17.653934 9.701634
## [775] 9.137385 11.133951 18.615122 17.295998 3.258297 8.464698
## [781] 12.165319 11.238344 9.108539 11.238344 8.228391 5.642657
## [787] 5.642657 6.774510 6.774510 12.537156 5.530478 10.932056
## [793] 7.114848 4.385505 8.907277 27.327103 4.529357 8.013251
## [799] 5.215520 5.215520 7.706956 5.938699 20.270738 3.759100
## [805] 2.488997 8.212975 7.670565 8.212975 8.648807 8.231166
## [811] 4.970159 8.618589 4.874312 5.197728 10.106415 4.874312
## [817] 4.894903 12.490257 4.865493 7.927125 3.455027 18.914190
## [823] 5.904417 5.904417 10.496669 4.006049 5.646430 4.006049
## [829] 12.597940 7.669173 3.846585 7.669173 16.228377 29.208155
## [835] 5.919295 4.713926 25.810750 25.810750 5.235988 7.380510
## [841] 6.597817 4.195649 9.505191 10.332031 8.363554 6.972547
## [847] 6.972547 4.079273 6.972547 6.722938 5.193155 5.193155
## [853] 15.416273 16.320424 16.320424 6.984951 16.320424 4.013691
## [859] 10.805777 4.714418 7.721219 64.918581 7.266167 6.710408
## [865] 7.721219 6.934873 4.494912 4.857461 4.714418 4.720066
## [871] 12.351718 10.055575 5.693339 5.864602 6.070615 4.148158
## [877] 5.248263 12.351718 5.303551 8.423991 5.830300 8.797458
## [883] 14.294670 8.423991 5.303551 3.913268 4.998098 11.156902
## [889] 5.978986 20.002272 5.841881 8.019418 6.202720 8.019418
## [895] 8.418608 4.240190 4.988120 4.240190 4.988120 15.448482
## [901] 5.592807 4.903942 4.903942 11.431730 11.431730 11.601731
## [907] 4.893472 6.384027 5.442468 7.881748 14.972518 30.448190
## [913] 5.483026 7.628412 7.881748 4.552082 16.362085 26.712716
## [919] 4.311073 15.341541 2.806473 4.311073 15.341541 26.712716
## [925] 4.980238 23.657043 25.945972 4.372993 4.980238 5.970708
## [931] 10.419858 4.002959 5.630227 4.002959 10.419858 6.795540
## [937] 6.795540 9.087393 7.881701 6.812156 7.830372 15.595957
## [943] 1.905350 2.147279 6.317657 6.342510 9.835501 7.476788
## [949] 8.616872 8.616872 8.616872 7.476788 3.783836 9.278689
## [955] 6.874949 6.281856 5.603925 4.751554 4.902361 3.018287
## [961] 6.063439 4.747043 5.275886 5.731900 6.063439 5.020407
## [967] 9.566694 9.153544 8.834133 3.274976 8.365011 8.365011
## [973] 4.126638 10.251363 10.815754 8.519601 8.519601 9.135841
## [979] 6.923368 14.573164 2.201698 7.821123 25.508096 2.201698
## [985] 14.573164 4.602743 7.660634 10.472565 9.548229 6.165284
## [991] 9.548229 8.850785 8.305399 8.850785 10.483490 2.137529
## [997] 13.259720 13.259720 21.053920 12.817516 3.734432 8.062510
## [1003] 5.377432 9.352772 4.692776 9.352772 5.377432 5.118976
## [1009] 4.830198 3.410139 8.707986 4.703969 11.724475 4.318942
## [1015] 6.232938 6.562456 4.942372 42.043752 42.043752 3.287452
## [1021] 12.931234 12.931234 4.374381 8.822726 4.031770 6.843650
## [1027] 7.554183 7.902706 10.018145 4.031770 5.263061 4.700558
## [1033] 7.663626 2.286899 3.677635 3.972810 10.790393 7.154505
## [1039] 14.176247 3.369223 9.988964 2.776431 3.369223 27.438466
## [1045] 8.429460 12.222280 10.914360 4.139890 2.733895 1.987666
## [1051] 4.139890 53.826513 9.156818 6.267952 8.852268 8.852268
## [1057] 9.091323 8.977048 10.233636 9.091323 8.785458 6.752119
## [1063] 4.456539 7.888279 7.796692 1.982821 6.272807 12.621655
## [1069] 12.621655 6.111257 7.773960 21.427847 6.924101 3.990897
## [1075] 21.427847 17.369324 6.747065 19.878454 19.878454 101.089151
## [1081] 10.244715 108.613590 6.348901 7.265461 6.348901 19.555261
## [1087] 7.872893 6.824327 9.318414 9.318414 20.416622 3.907595
## [1093] 3.003776 5.018200 11.274804 6.854745 11.274804 5.168131
## [1099] 19.018359 5.168131 10.975046 3.492852 3.702381 3.492852
## [1105] 5.840225 10.102601 8.926458 4.748594 5.918824 9.402094
## [1111] 6.797680 15.989585 7.150414 8.814705 30.318254 4.342757
## [1117] 4.342757 4.342757 13.644283 9.689570 13.041653 7.905775
## [1123] 11.465155 5.084681 8.873578 7.950224 16.018854 7.825977
## [1129] 4.726832 4.642709 11.210487 28.704822 11.050634 10.458070
## [1135] 4.366043 5.945824 4.330081 4.330081 11.991354 4.132735
## [1141] 6.130172 3.079421 6.781024 13.146287 6.850764 13.241204
## [1147] 9.795564 13.311477 3.548016 5.958810 7.601534 7.807763
## [1153] 3.857538 2.045075 20.075006 3.857538 14.741850 18.839398
## [1159] 17.731888 2.353418 4.199550 5.671243 6.378182 5.393826
## [1165] 5.393826 39.869113 14.977332 7.795900 12.472364 2.922136
## [1171] 3.311191 4.435286 5.320585 5.886313 5.886313 18.923050
## [1177] 14.506232 10.520486 20.397184 2.753799 2.753799 7.969111
## [1183] 17.346842 3.816227 14.356216 7.612062 24.775631 7.612062
## [1189] 14.356216 12.200143 10.176856 12.968230 8.750589 12.719748
## [1195] 4.795924 5.620778 3.883760 8.185645 9.700441 3.883760
## [1201] 8.185645 5.426538 3.383861 13.679311 7.391140 7.391140
## [1207] 7.391140 19.319344 7.391140 10.831850 3.568886 4.230792
## [1213] 3.568886 3.639395 6.862718 6.385954 5.032880 13.980736
## [1219] 1.982496 8.516264 8.573493 8.573493 5.495488 4.778418
## [1225] 8.290598 10.374209 18.669039 2.037357 24.189440 2.921354
## [1231] 7.388401 15.514670 2.921354 18.340166 13.308073 88.046696
## [1237] 6.114670 13.308073 4.843559 7.066570 16.951779 4.462562
## [1243] 5.663364 7.005681 88.030750 1.948751 8.674657 1.948751
## [1249] 3.855588 3.854634 3.854634 2.806337 5.214751 10.089405
## [1255] 3.338380 6.350956 22.563337 6.428218 5.260289 5.260289
## [1261] 53.543724 14.080716 6.688332 4.986624 5.963262 1.874085
## [1267] 1.874085 7.667112 5.630267 21.478027 21.731594 5.231948
## [1273] 6.209244 17.555781 4.897162 4.595104 15.307635 4.969591
## [1279] 4.595104 7.542328 2.767931 2.767931 4.994775 1.172129
## [1285] 2.226633 6.837525 10.271829 16.933974 29.026836 29.026836
## [1291] 5.154205 4.256651 7.600990 4.567027 4.256651 23.595001
## [1297] 23.595001 5.091754 7.973318 54.283687 12.566965 9.908922
## [1303] 4.729834 5.432349 10.423587 3.412653 5.261872 4.058581
## [1309] 5.261872 5.086574 3.412653 8.564025 23.528057 4.089649
## [1315] 4.399815 4.340005 38.598983 4.180030 4.340005 63.361803
## [1321] 6.302062 34.357871 7.875999 5.798218 2.474199 2.474199
## [1327] 2.474199 2.474199 3.999350 4.001273 4.001273 13.579877
## [1333] 11.959669 4.322490 9.473408 8.432457 6.899140 6.899140
## [1339] 6.899140 4.220861 4.220861 4.220861 4.119553 4.220861
## [1345] 6.595151 2.186578 4.641476 5.338101 5.338101 3.883063
## [1351] 6.584012 9.471055 5.457164 5.457164 3.537833 9.201577
## [1357] 8.958750 7.360021 21.917462 9.634371 2.563171 3.911709
## [1363] 9.634371 6.577959 6.819857 6.744233 4.344727 27.677993
## [1369] 6.027845 7.429198 57.642437 18.893239 57.642437 9.678288
## [1375] 27.470929 7.830536 13.679873 15.625637 3.933112 3.503903
## [1381] 3.503903 7.105700 5.491173 5.491173 6.958876 12.039234
## [1387] 2.967462 2.967462 13.838414 17.161213 9.451741 2.386711
## [1393] 9.213888 1.793944 6.291024 3.957826 3.942456 6.602987
## [1399] 2.495049 3.950359 11.050873 11.050873 8.964232 18.488464
## [1405] 7.937882 5.917028 13.868771 14.913228 15.219415 14.913228
## [1411] 12.541443 10.316052 13.868771 14.357669 8.964370 9.961468
## [1417] 8.964370 6.044411 9.993142 10.407810 9.993142 3.843204
## [1423] 13.303518 6.689971 6.350140 6.350140 17.682775 3.712998
## [1429] 7.081661 12.626892 1.418053 4.194961 11.053066 5.113558
## [1435] 103.981865 103.981865 11.574052 17.760918 5.036355 7.324487
## [1441] 6.174224 11.225929 5.207937 5.357921 7.473872 11.222294
## [1447] 5.207937 5.369846 11.783230 6.532206 6.174224 7.561420
## [1453] 11.412010 6.364998 8.001066 8.423221 13.118457 6.364998
## [1459] 4.640842 6.550960 13.934424 6.847841 2.183912 3.221454
## [1465] 7.108802 7.108802 10.882296 14.216691 10.882296 10.459053
## [1471] 18.602788 9.910586 11.590770 3.853403 82.498039 20.313600
## [1477] 82.498039 20.470162 13.342375 11.658084 8.803689 11.658084
## [1483] 18.317709 5.740438 8.831716 2.952996 3.937730 5.629620
## [1489] 9.688688 6.450446 10.543583 9.688688 6.450670 14.475586
## [1495] 7.044119 6.288124 14.475586 4.625728 7.159155 4.625728
## [1501] 6.405915 12.276318 1.578328 8.910358 12.573498 6.205606
## [1507] 5.489014 12.573498 26.279547 7.360687 8.067310 4.216524
## [1513] 5.448569 7.636692 11.996160 11.927128 11.074038 7.553584
## [1519] 7.057738 3.269965 7.553584 6.886501 11.074038 3.848948
## [1525] 5.144173 3.907484 4.390757 3.952071 5.563905 12.522028
## [1531] 3.819684 5.640142 7.382875 7.297618 2.899841 1.883498
## [1537] 5.701972 5.176219 6.075702 9.226779 8.117967 6.130813
## [1543] 11.964694 6.948615 6.244210 9.809232 2.953847 6.343615
## [1549] 5.016615 4.923703 5.274732 4.863939 7.394950 4.620242
## [1555] 6.527209 2.557964 7.081859 6.527209 46.573968 9.307936
## [1561] 9.307936 9.307936 3.787787 3.787787 3.787787 7.002732
## [1567] 16.799611 3.787787 4.772647 2.882490 21.265520 4.568784
## [1573] 9.103877 7.738962 106.341768 7.888124 10.384994 9.796654
## [1579] 4.156168 4.966215 3.972005 4.966215 7.002044 7.426114
## [1585] 12.562691 7.019266 8.113546 4.106200 16.271469 22.224284
## [1591] 8.964462 8.706915 7.226201 8.446444 6.118501 11.211489
## [1597] 7.226201 9.037668 12.270976Let’s perform DBSCAN to detect outliers in data. We can observe data points in green traingle and circle as outliers red are normal data points.
library(fpc)## Warning: package 'fpc' was built under R version 4.3.2# Prepare the data (if not already prepared)
df_cm <- colMeans(df_new)
df_S <- cov(df_new)
df_d <- apply(df_new, MARGIN = 1, function(df_new) t(df_new - df_cm) %*% solve(df_S) %*% (df_new - df_cm))
# DBSCAN
dbscan_result <- dbscan(df_d, eps = 0.5, MinPts = 5)
# Summary of DBSCAN result
summary(dbscan_result)## Length Class Mode
## cluster 1599 -none- numeric
## eps 1 -none- numeric
## MinPts 1 -none- numeric
## isseed 1599 -none- logical# Plot clusters
plot(dbscan_result, df_d)
Goal is to find t value and p-value
t-value is 19.292, indicating a large difference between the means of the two groups (Varialbes used: alcohol and compared with quality, target variable which is either good or bad)
p-values is very low indicating strong correlation of quality of wine with respect to alcohol level in wine.
hence we have seem to have map good relation based on t and p values
Confidence interval is 95 indicating difference present between interval 0.83 and 1.022
with(data=df,t.test(alcohol[quality=="good"],alcohol[quality=="bad"],var.equal=TRUE))##
## Two Sample t-test
##
## data: alcohol[quality == "good"] and alcohol[quality == "bad"]
## t = 19.292, df = 1597, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.8341451 1.0229564
## sample estimates:
## mean of x mean of y
## 10.855029 9.926478Attributes used for Two sample t-test
t-value is -13.566, indicating a substantial difference between the means of the two groups.
p-value is extremely low (< 0.00000000000000022), suggesting strong evidence against the null hypothesis. This means there is a significant difference in the mean volatile acidity between the “good” and “bad” quality groups.
95% confidence interval for the difference in means is (-0.13203601, -0.09867697). This interval suggests that the mean volatile acidity for the “good” quality group is expected to be between 0.13203601 and 0.09867697 units lower than the mean volatile acidity
Overall, this output indicates a statistically significant difference in the mean volatile acidity between wines of “good” and “bad” quality, with “good” quality wines having, on average, lower volatile acidity compared to “bad” quality wines.
with(data=df,t.test(`volatile acidity`[quality=="good"],`volatile acidity`[quality=="bad"],var.equal=TRUE))##
## Two Sample t-test
##
## data: `volatile acidity`[quality == "good"] and `volatile acidity`[quality == "bad"]
## t = -13.566, df = 1597, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13203601 -0.09867697
## sample estimates:
## mean of x mean of y
## 0.4741462 0.5895027t-value is -4.4021, indicating a notable but not very high difference between the means of the two groups. Hence not as good as previous two variables
p-value is 0.00001143, which is less than the significance level (usually 0.05), indicating strong evidence against the null hypothesis. This means there is a significant difference in the mean chlorides content between the “good” and “bad” quality groups.
Overall, this output indicates a statistically significant difference in the mean chlorides content between wines of “good” and “bad” quality, with “good” quality wines having, on average, lower chlorides content compared to “bad” quality wines.
with(data=df,t.test(chlorides[quality=="good"],chlorides[quality=="bad"],var.equal=TRUE))##
## Two Sample t-test
##
## data: chlorides[quality == "good"] and chlorides[quality == "bad"]
## t = -4.4021, df = 1597, p-value = 0.00001143
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.014930462 -0.005726395
## sample estimates:
## mean of x mean of y
## 0.08266082 0.09298925t-value is -0.13044, indicating a very small difference between the means of the two groups. Hence not good to understand the dependent variable using pH as independent variable as it reveals no information.
p-value is 0.8962, which is much greater than the significance level (usually 0.05), indicating weak evidence against the null hypothesis. This means there is no significant difference in the mean pH levels between the “good” and “bad” quality groups.
Comparing to the previous values, in this case, there is no significant difference in the mean pH levels between wines of “good” and “bad” quality. The t-value is close to zero, the p-value is high, and the confidence interval includes zero, indicating that any observed difference in mean pH levels between the two groups is likely due to random chance.
with(data=df,t.test(pH[quality=="good"],pH[quality=="bad"],var.equal=TRUE))##
## Two Sample t-test
##
## data: pH[quality == "good"] and pH[quality == "bad"]
## t = -0.13044, df = 1597, p-value = 0.8962
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01619705 0.01417715
## sample estimates:
## mean of x mean of y
## 3.310643 3.311653The F-statistic of approximately 2.1294, accompanied by a small p-value (< 0.00000000000000022) and a 95% confidence interval for the ratio of variances (1.852111 to 2.446455), suggests strong evidence against the null hypothesis that the variances of alcohol levels between the “good” and “bad” quality groups are equal. Therefore, we infer that there is a statistically significant difference in the variability of alcohol levels between these two quality groups in the dataset.
var.test(df$alcohol[df$quality=="good"],df$alcohol[df$quality=="bad"])##
## F test to compare two variances
##
## data: df$alcohol[df$quality == "good"] and df$alcohol[df$quality == "bad"]
## F = 2.1294, num df = 854, denom df = 743, p-value < 0.00000000000000022
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.852111 2.446455
## sample estimates:
## ratio of variances
## 2.129366attach(df)## The following objects are masked from df (pos = 5):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidityScaling data again to improve score of f and p value
matstand <- scale(df[,1:11]) # Scaling data again to improve score of f and p value
matstand## fixed acidity volatile acidity citric acid residual sugar chlorides
## [1,] -0.52819437 0.96157585 -1.391037103 -0.453076665 -0.243630469
## [2,] -0.29845406 1.96682715 -1.391037103 0.043402567 0.223805180
## [3,] -0.29845406 1.29665962 -1.185699492 -0.169374247 0.096322731
## [4,] 1.65433853 -1.38401051 1.483689439 -0.453076665 -0.264877544
## [5,] -0.52819437 0.96157585 -1.391037103 -0.453076665 -0.243630469
## [6,] -0.52819437 0.73818667 -1.391037103 -0.524002270 -0.264877544
## [7,] -0.24101899 0.40310291 -1.083030687 -0.665853479 -0.392359993
## [8,] -0.58562945 0.68233938 -1.391037103 -0.949555897 -0.477348293
## [9,] -0.29845406 0.29140832 -1.288368298 -0.382151061 -0.307371694
## [10,] -0.47075929 -0.15537004 0.457001388 2.525798726 -0.349865844
## [11,] -0.93023990 0.29140832 -0.980361882 -0.524002270 0.202558106
## [12,] -0.47075929 -0.15537004 0.457001388 2.525798726 -0.349865844
## [13,] -1.56202574 0.48687385 -1.391037103 -0.665853479 0.032581506
## [14,] -0.29845406 0.45895020 0.097660571 -0.665853479 0.563758380
## [15,] 0.33333178 0.51479750 -0.467017857 0.894509821 1.881077028
## [16,] 0.33333178 0.51479750 -0.415683455 0.965435426 1.753594578
## [17,] 0.10359147 -1.38401051 1.483689439 -0.524002270 0.096322731
## [18,] -0.12614884 0.17971373 0.046326168 -0.594927874 5.960515421
## [19,] -0.52819437 0.34725561 -0.980361882 1.320063449 -0.031159719
## [20,] -0.24101899 -1.16062133 1.227017426 -0.524002270 5.386844397
## [21,] 0.33333178 -1.71909428 1.073014219 -0.524002270 -0.222383394
## [22,] -0.41332422 -0.76969027 0.200329376 -0.169374247 -0.116148019
## [23,] -0.24101899 -0.54630110 -0.313014650 -0.665853479 0.393781780
## [24,] 0.10359147 -0.21121733 -0.826358675 -0.169374247 -0.073653869
## [25,] -0.81536975 -0.71384298 -0.672355467 -0.098448642 -0.052406794
## [26,] -1.15998021 -0.76969027 -0.569686662 -0.807704688 -0.158642169
## [27,] -0.41332422 -0.65799569 -0.159011442 -0.524002270 -0.158642169
## [28,] -0.24101899 -0.54630110 -0.313014650 -0.665853479 0.393781780
## [29,] -0.70049960 1.01742314 -1.391037103 -0.453076665 -0.158642169
## [30,] -0.29845406 0.65441573 -1.391037103 -0.382151061 -0.116148019
## [31,] -0.93023990 0.82195761 -1.031696285 -0.098448642 0.032581506
## [32,] -0.81536975 0.87780491 -1.391037103 -0.027523038 0.372534705
## [33,] -0.01127868 0.71026303 -0.775024272 -0.169374247 -0.094900944
## [34,] -0.81536975 0.43102655 -0.775024272 5.788376536 -0.307371694
## [35,] -1.79176605 -1.16062133 -0.107677039 -0.524002270 0.330040555
## [36,] -0.29845406 0.65441573 -1.391037103 2.100245099 -0.031159719
## [37,] -0.29845406 0.40310291 -0.672355467 -0.098448642 -0.031159719
## [38,] -0.12614884 -0.82553757 0.046326168 -0.311225456 -0.456101218
## [39,] -1.50459067 3.36300951 -0.929027480 -0.736779083 1.796088728
## [40,] -0.58562945 -0.43460651 0.457001388 2.383947517 -0.286124619
## [41,] -0.58562945 -0.43460651 0.457001388 2.383947517 -0.286124619
## [42,] 0.27589670 0.45895020 0.148994973 0.185253776 0.011334431
## [43,] -0.47075929 -0.21121733 -0.364349052 0.043402567 5.195620722
## [44,] -0.12614884 0.73818667 -0.261680247 -0.240299852 -0.392359993
## [45,] -0.87280483 0.79403397 -1.288368298 -0.524002270 -0.796054418
## [46,] -2.13637651 -0.04367545 -0.621021065 -0.311225456 -0.711066118
## [47,] -0.35588914 2.27398727 0.816342206 -0.240299852 0.563758380
## [48,] 0.21846162 -1.32816322 1.278351829 -0.665853479 0.542511305
## [49,] -1.10254513 -0.71384298 -0.210345844 -0.665853479 -0.456101218
## [50,] -1.56202574 -1.21646863 0.508335791 -0.807704688 -0.286124619
## [51,] 0.27589670 0.73818667 -0.056342637 -0.594927874 -0.286124619
## [52,] -0.98767498 -0.04367545 -1.185699492 -0.240299852 -0.392359993
## [53,] -0.98767498 -0.15537004 -1.185699492 -0.311225456 -0.413607068
## [54,] 0.16102655 -0.82553757 0.457001388 0.327104985 -0.137395094
## [55,] -0.41332422 -0.09952274 -0.621021065 0.185253776 0.478770080
## [56,] -0.35588914 0.51479750 -1.185699492 0.894509821 -0.073653869
## [57,] 1.07998777 -0.60214839 1.535023841 0.610807403 -0.371112918
## [58,] -0.47075929 0.57064479 -0.775024272 1.816542681 0.500017155
## [59,] -0.29845406 0.34725561 -0.467017857 -0.169374247 -0.243630469
## [60,] -0.58562945 -0.76969027 0.200329376 -0.098448642 -0.286124619
## [61,] 0.27589670 -0.71384298 0.662338998 -0.240299852 -0.179889244
## [62,] -0.35588914 0.90572856 1.124348621 -0.524002270 0.585005455
## [63,] -0.47075929 -0.04367545 -0.569686662 -0.453076665 -0.052406794
## [64,] -0.75793468 1.15704138 -1.134365090 -0.382151061 -0.137395094
## [65,] -0.64306452 1.10119409 -1.134365090 1.497377460 -0.031159719
## [66,] -0.64306452 1.10119409 -1.134365090 1.497377460 -0.031159719
## [67,] -0.47075929 -0.04367545 -0.826358675 -0.736779083 -0.179889244
## [68,] -0.98767498 0.98949950 -1.031696285 -0.665853479 -0.243630469
## [69,] 0.56307208 -1.16062133 1.535023841 -0.382151061 -0.286124619
## [70,] -0.18358391 0.98949950 -1.134365090 -0.453076665 -0.286124619
## [71,] -0.35588914 0.57064479 -0.980361882 -0.453076665 -0.243630469
## [72,] -0.35588914 0.79403397 -0.210345844 -0.311225456 0.011334431
## [73,] -0.35588914 0.90572856 -0.261680247 -0.453076665 -0.073653869
## [74,] -0.01127868 0.82195761 -0.056342637 -0.311225456 -0.073653869
## [75,] 0.79281239 -1.16062133 1.381020634 -0.027523038 0.138816881
## [76,] 0.27589670 -0.65799569 1.894364659 -0.240299852 0.117569806
## [77,] 0.27589670 -0.65799569 1.894364659 -0.240299852 0.117569806
## [78,] -0.87280483 1.43627785 -1.391037103 -0.098448642 0.351287630
## [79,] -0.93023990 1.24081232 -0.775024272 -0.382151061 -0.031159719
## [80,] -0.01127868 0.54272114 -0.364349052 -0.736779083 -0.158642169
## [81,] -1.21741529 -0.43460651 -0.364349052 -0.665853479 -0.392359993
## [82,] -0.29845406 -0.54630110 2.202371074 -0.453076665 8.000234617
## [83,] -0.52819437 -0.15537004 1.021679816 -0.382151061 -0.031159719
## [84,] -0.58562945 0.79403397 -0.056342637 -0.524002270 6.661668895
## [85,] -1.15998021 -1.27231592 1.073014219 -0.524002270 -0.392359993
## [86,] -0.81536975 0.12386643 -0.621021065 -0.240299852 -0.243630469
## [87,] 0.16102655 -0.21121733 0.046326168 -0.453076665 0.478770080
## [88,] -0.35588914 -0.21121733 -0.056342637 -0.453076665 -0.541089518
## [89,] 0.56307208 -0.76969027 0.867676609 -0.311225456 0.415028855
## [90,] -0.75793468 0.51479750 -0.980361882 -0.524002270 -0.243630469
## [91,] -0.24101899 -0.04367545 -0.056342637 -0.453076665 -0.179889244
## [92,] 0.16102655 -0.21121733 0.046326168 -0.453076665 0.478770080
## [93,] 0.16102655 -0.21121733 0.097660571 -0.382151061 0.478770080
## [94,] -0.35588914 -0.21121733 -0.056342637 -0.453076665 -0.541089518
## [95,] -1.90663620 2.74868927 -1.185699492 -0.807704688 -0.902289793
## [96,] -2.07894143 0.40310291 -0.518352260 -0.169374247 -0.626077818
## [97,] -0.87280483 1.38043056 -1.391037103 0.327104985 0.308793480
## [98,] -0.75793468 -0.15537004 -0.107677039 -0.382151061 -0.371112918
## [99,] -0.41332422 2.07852174 -1.083030687 -0.027523038 -0.179889244
## [100,] -0.12614884 0.09594279 -0.467017857 -0.453076665 -0.158642169
## [101,] -0.01127868 0.45895020 0.148994973 -0.311225456 -0.073653869
## [102,] -0.29845406 -0.15537004 0.148994973 -0.453076665 -0.264877544
## [103,] -0.12614884 0.09594279 -0.467017857 -0.453076665 -0.158642169
## [104,] -0.12614884 0.26348467 -0.261680247 -0.311225456 -0.222383394
## [105,] -0.64306452 -0.21121733 -0.159011442 -0.240299852 -0.371112918
## [106,] -0.12614884 0.26348467 -0.261680247 -0.311225456 -0.222383394
## [107,] -0.29845406 -0.65799569 2.099702269 -0.594927874 8.063975842
## [108,] -1.21741529 0.57064479 0.200329376 -0.594927874 0.011334431
## [109,] -0.18358391 -1.10477404 1.329686231 -0.027523038 0.075075656
## [110,] -0.12614884 1.43627785 1.278351829 -0.382151061 0.733734980
## [111,] -0.29845406 0.17971373 -0.415683455 -0.524002270 0.351287630
## [112,] 0.04615639 0.51479750 -0.929027480 -0.240299852 -0.073653869
## [113,] 0.04615639 0.40310291 -0.877693077 -0.240299852 -0.052406794
## [114,] 1.02255269 -1.21646863 0.867676609 -0.169374247 -0.158642169
## [115,] -0.29845406 0.17971373 -0.415683455 -0.524002270 0.351287630
## [116,] 0.62050716 -0.71384298 0.200329376 -0.240299852 0.053828581
## [117,] -0.01127868 0.06801914 0.046326168 -0.453076665 -0.222383394
## [118,] -0.29845406 0.17971373 -0.775024272 -0.382151061 -0.116148019
## [119,] 0.27589670 0.12386643 -1.185699492 -0.240299852 0.669993755
## [120,] -0.75793468 0.90572856 -0.980361882 -0.524002270 0.202558106
## [121,] -0.58562945 3.02792574 -0.929027480 -0.594927874 1.923571178
## [122,] 0.27589670 0.12386643 -1.185699492 -0.240299852 0.669993755
## [123,] -0.58562945 0.93365220 -1.391037103 -0.027523038 -0.264877544
## [124,] -0.18358391 1.01742314 -1.391037103 0.043402567 -0.158642169
## [125,] -0.29845406 -0.15537004 -0.518352260 -0.665853479 -0.116148019
## [126,] 0.39076685 0.51479750 -1.185699492 -0.453076665 1.243664779
## [127,] -0.06871376 4.47995539 -1.391037103 -0.594927874 -0.137395094
## [128,] -0.12614884 4.47995539 -1.391037103 -0.524002270 -0.116148019
## [129,] -0.18358391 0.34725561 -0.569686662 -0.524002270 -0.477348293
## [130,] -1.27485036 -0.82553757 -0.621021065 -0.524002270 -0.328618769
## [131,] -0.18358391 1.21288867 1.483689439 -0.382151061 0.648746680
## [132,] -1.56202574 -0.15537004 -0.929027480 -0.169374247 -0.817301493
## [133,] -1.56202574 -0.15537004 -0.929027480 -0.169374247 -0.817301493
## [134,] -0.98767498 -0.15537004 -1.339702700 -0.736779083 -0.583583668
## [135,] -0.24101899 2.86038386 -1.134365090 -0.240299852 -0.073653869
## [136,] 0.04615639 1.21288867 -0.826358675 -0.453076665 0.053828581
## [137,] -0.01127868 1.04534679 -0.621021065 -0.524002270 0.032581506
## [138,] -0.64306452 -0.63007204 0.457001388 -0.382151061 -0.137395094
## [139,] -0.29845406 0.17971373 -0.415683455 -0.311225456 -0.137395094
## [140,] -0.29845406 0.17971373 -0.415683455 -0.382151061 -0.137395094
## [141,] 0.04615639 1.21288867 -0.826358675 -0.453076665 0.053828581
## [142,] -0.01127868 1.04534679 -0.621021065 -0.524002270 0.032581506
## [143,] -1.79176605 -1.04892675 -1.391037103 -0.524002270 -0.796054418
## [144,] -1.15998021 -0.76969027 -0.980361882 -0.594927874 -0.456101218
## [145,] -1.79176605 -1.04892675 -1.391037103 -0.524002270 -0.796054418
## [146,] -0.12614884 0.79403397 1.432355036 -0.524002270 0.627499605
## [147,] -1.44715559 0.84988126 -1.288368298 -0.524002270 -0.009912644
## [148,] -0.41332422 -0.21121733 -0.056342637 -0.665853479 3.155901525
## [149,] -0.81536975 -0.21121733 -0.877693077 -0.169374247 -0.286124619
## [150,] -0.06871376 -0.71384298 0.867676609 0.185253776 0.032581506
## [151,] -0.58562945 -1.10477404 1.021679816 -0.311225456 -0.222383394
## [152,] 0.50563700 -0.04367545 3.742403150 0.610807403 11.102307562
## [153,] -0.47075929 0.40310291 -1.237033895 -0.524002270 0.160063956
## [154,] -0.47075929 0.40310291 -1.237033895 -0.524002270 0.160063956
## [155,] -0.70049960 -0.54630110 0.765007803 2.100245099 -0.371112918
## [156,] -0.70049960 -0.54630110 0.765007803 2.100245099 -0.349865844
## [157,] -0.70049960 -0.54630110 0.765007803 2.100245099 -0.371112918
## [158,] -0.70049960 -0.54630110 0.765007803 2.100245099 -0.349865844
## [159,] -0.70049960 0.84988126 -1.391037103 -0.240299852 -0.307371694
## [160,] -0.87280483 0.40310291 -0.467017857 -0.453076665 -0.179889244
## [161,] -0.41332422 2.35775821 -1.237033895 -0.382151061 0.053828581
## [162,] -0.41332422 0.84988126 -1.288368298 -0.878630293 -0.328618769
## [163,] -0.29845406 0.01217185 -1.185699492 -0.594927874 -0.243630469
## [164,] -0.52819437 0.40310291 -0.056342637 3.376905981 -0.371112918
## [165,] -0.58562945 0.34725561 -0.056342637 3.305980376 -0.371112918
## [166,] -0.29845406 0.57064479 1.073014219 -0.594927874 0.266299330
## [167,] -0.87280483 0.62649208 -0.877693077 -0.311225456 -0.052406794
## [168,] -0.58562945 0.12386643 -1.237033895 -0.665853479 -0.328618769
## [169,] -0.87280483 0.57064479 -1.031696285 -0.311225456 0.032581506
## [170,] -0.47075929 0.98949950 -0.159011442 -0.524002270 5.790538821
## [171,] -0.24101899 1.99475079 -1.237033895 -0.524002270 -0.626077818
## [172,] -0.18358391 -0.60214839 -0.518352260 -0.382151061 -0.307371694
## [173,] -0.18358391 -0.60214839 -0.518352260 -0.382151061 -0.307371694
## [174,] -0.52819437 0.51479750 -1.134365090 -0.453076665 -0.413607068
## [175,] -0.58562945 -0.82553757 -0.313014650 -0.382151061 -0.158642169
## [176,] -0.81536975 -0.15537004 -1.185699492 -0.736779083 -0.052406794
## [177,] -0.58562945 -0.82553757 -0.313014650 -0.382151061 -0.158642169
## [178,] -0.47075929 -0.04367545 0.765007803 -0.169374247 -0.009912644
## [179,] -0.75793468 1.54797244 -1.391037103 -0.027523038 -0.413607068
## [180,] 0.27589670 0.45895020 -0.672355467 -0.098448642 -0.434854143
## [181,] 0.27589670 0.45895020 -0.672355467 -0.098448642 -0.434854143
## [182,] 0.33333178 0.45895020 1.124348621 -0.382151061 3.878302074
## [183,] -0.64306452 1.12911773 -1.288368298 -0.027523038 -0.243630469
## [184,] -0.87280483 0.45895020 -0.364349052 -0.524002270 -0.222383394
## [185,] -0.93023990 0.51479750 -0.313014650 -0.453076665 -0.179889244
## [186,] 0.33333178 -1.21646863 1.535023841 -0.382151061 0.500017155
## [187,] -0.52819437 -0.76969027 1.073014219 -0.382151061 -0.116148019
## [188,] -0.35588914 0.98949950 -0.877693077 0.043402567 -0.073653869
## [189,] -0.24101899 -0.15537004 0.302998181 -0.382151061 -0.073653869
## [190,] -0.24101899 -0.21121733 0.251663778 -0.453076665 -0.116148019
## [191,] -0.06871376 -0.15537004 0.405666986 0.256179380 -0.222383394
## [192,] -1.10254513 -0.88138486 -0.107677039 -0.453076665 -0.286124619
## [193,] -0.87280483 0.57064479 -0.775024272 0.894509821 0.245052255
## [194,] -0.41332422 0.12386643 -0.313014650 -0.240299852 -0.349865844
## [195,] -0.41332422 0.12386643 -0.313014650 -0.240299852 -0.349865844
## [196,] -0.29845406 0.34725561 0.302998181 -0.382151061 -0.286124619
## [197,] -0.58562945 0.29140832 0.148994973 -0.098448642 -0.286124619
## [198,] 1.82664376 -1.27231592 1.689027049 -0.382151061 -0.434854143
## [199,] -1.67689590 1.71551432 -0.980361882 -0.949555897 -0.881042718
## [200,] -0.81536975 3.13962033 -1.083030687 -0.311225456 -0.562336593
## [201,] 0.73537731 -1.16062133 1.021679816 -0.807704688 -0.668571968
## [202,] 0.27589670 -0.88138486 1.073014219 -0.311225456 0.202558106
## [203,] -0.87280483 -0.15537004 -0.826358675 -0.736779083 -0.264877544
## [204,] -0.75793468 -0.60214839 0.405666986 -0.665853479 0.011334431
## [205,] -0.75793468 -0.54630110 0.457001388 -0.665853479 0.032581506
## [206,] 2.57329975 -1.27231592 2.407708684 0.043402567 0.160063956
## [207,] 2.57329975 -1.27231592 2.407708684 0.043402567 0.160063956
## [208,] -0.29845406 0.23556102 0.200329376 -0.524002270 -0.392359993
## [209,] -0.29845406 -0.49045380 0.046326168 0.114328171 0.266299330
## [210,] 1.53946838 -1.27231592 1.586358244 -0.311225456 -0.711066118
## [211,] 0.79281239 0.01217185 1.689027049 -0.382151061 -1.029772242
## [212,] -0.18358391 1.10119409 -0.159011442 0.185253776 -0.094900944
## [213,] 1.88407884 -0.49045380 1.894364659 -0.311225456 -0.604830743
## [214,] -0.06871376 0.23556102 -0.056342637 -0.240299852 -0.583583668
## [215,] -0.29845406 1.15704138 -0.980361882 -0.098448642 0.096322731
## [216,] -0.75793468 -0.21121733 1.124348621 2.171170703 -0.583583668
## [217,] 0.21846162 0.54272114 -0.569686662 -0.382151061 0.287546405
## [218,] -0.12614884 1.10119409 -0.261680247 -0.240299852 -0.328618769
## [219,] -0.47075929 -0.21121733 -0.415683455 -0.453076665 -0.243630469
## [220,] -0.29845406 0.01217185 0.302998181 -0.098448642 -0.158642169
## [221,] -0.29845406 -1.04892675 0.508335791 -0.382151061 -0.116148019
## [222,] -0.52819437 0.01217185 -0.056342637 -0.382151061 0.287546405
## [223,] -0.87280483 0.45895020 -1.185699492 -0.736779083 -0.647324893
## [224,] 0.16102655 0.65441573 -0.107677039 -0.382151061 -0.094900944
## [225,] 0.04615639 0.59856844 0.457001388 -0.382151061 0.032581506
## [226,] -0.35588914 -0.54630110 -0.107677039 0.043402567 -0.307371694
## [227,] 0.33333178 0.34725561 1.175683024 -0.382151061 5.301856097
## [228,] 0.39076685 1.63174338 -0.672355467 0.043402567 0.032581506
## [229,] -0.35588914 -0.54630110 -0.107677039 0.043402567 -0.307371694
## [230,] -0.81536975 -0.04367545 -0.107677039 0.043402567 -0.137395094
## [231,] -1.79176605 -0.26706463 -1.185699492 -0.665853479 -0.711066118
## [232,] -0.18358391 -0.82553757 -1.083030687 -0.524002270 -0.201136319
## [233,] 0.10359147 -0.88138486 -0.364349052 0.185253776 0.053828581
## [234,] -0.81536975 -0.04367545 -0.107677039 0.043402567 -0.137395094
## [235,] -0.06871376 2.63699468 -0.929027480 -0.169374247 -0.477348293
## [236,] -0.64306452 0.57064479 -1.391037103 -0.453076665 0.202558106
## [237,] -0.64306452 0.57064479 -1.391037103 -0.453076665 0.202558106
## [238,] -0.64306452 0.65441573 -1.391037103 -0.453076665 0.202558106
## [239,] -0.64306452 0.57064479 -1.391037103 -0.453076665 0.202558106
## [240,] -0.06871376 2.63699468 -0.929027480 -0.169374247 -0.477348293
## [241,] 0.33333178 0.59856844 0.508335791 -0.594927874 3.729572550
## [242,] 2.11381914 -0.82553757 1.483689439 -0.311225456 0.117569806
## [243,] -0.35588914 0.29140832 -0.877693077 -0.524002270 0.308793480
## [244,] 3.83687143 -1.77494157 0.867676609 -0.240299852 -0.264877544
## [245,] 3.83687143 -1.77494157 0.867676609 -0.240299852 -0.264877544
## [246,] -0.58562945 0.73818667 -1.391037103 -0.382151061 -0.073653869
## [247,] -0.70049960 0.84988126 -1.031696285 -0.453076665 -0.264877544
## [248,] -0.06871376 0.40310291 -0.518352260 -0.169374247 -0.328618769
## [249,] -0.35588914 0.01217185 -1.083030687 -0.594927874 -0.286124619
## [250,] -0.58562945 0.73818667 -1.391037103 -0.382151061 -0.073653869
## [251,] 1.42459823 -1.16062133 0.867676609 -0.665853479 -0.519842443
## [252,] -0.70049960 0.40310291 -1.391037103 -0.524002270 -0.286124619
## [253,] 1.59690346 -0.99307945 1.073014219 0.398030589 0.053828581
## [254,] -0.35588914 1.38043056 0.765007803 -0.453076665 0.096322731
## [255,] -0.70049960 0.40310291 -1.391037103 -0.524002270 -0.286124619
## [256,] -0.18358391 0.23556102 -0.210345844 0.468956194 -0.307371694
## [257,] 0.62050716 -1.04892675 0.508335791 -0.240299852 -0.264877544
## [258,] -0.98767498 0.93365220 -1.391037103 -0.311225456 -0.264877544
## [259,] -0.35588914 -0.65799569 2.510377489 -0.524002270 11.123554637
## [260,] 0.96511762 -1.21646863 1.021679816 0.043402567 -0.052406794
## [261,] -0.24101899 -1.10477404 -0.210345844 -0.594927874 -0.222383394
## [262,] -0.75793468 2.49737644 -1.185699492 -0.382151061 -0.009912644
## [263,] -0.18358391 -0.04367545 -1.237033895 -0.594927874 -0.371112918
## [264,] -0.24101899 -0.88138486 -0.210345844 -0.524002270 -0.222383394
## [265,] 2.40099452 0.17971373 1.124348621 -0.098448642 -0.498595368
## [266,] 1.99894899 -1.49570510 1.278351829 -0.524002270 -0.349865844
## [267,] -0.12614884 1.91097985 -1.391037103 0.539881799 0.181311031
## [268,] -0.24101899 -0.99307945 0.970345414 0.752658612 -0.201136319
## [269,] -0.81536975 0.06801914 -1.185699492 0.327104985 -0.222383394
## [270,] 1.82664376 -1.94248346 1.227017426 1.036361030 0.351287630
## [271,] -0.24101899 0.09594279 -1.083030687 1.036361030 -0.009912644
## [272,] 1.82664376 -1.94248346 1.227017426 1.036361030 0.351287630
## [273,] 1.48203330 -0.88138486 1.586358244 1.036361030 -0.349865844
## [274,] 0.04615639 1.04534679 -0.364349052 -0.098448642 -0.243630469
## [275,] -0.47075929 0.68233938 -0.467017857 3.164129167 0.011334431
## [276,] -0.24101899 0.09594279 -1.083030687 1.036361030 -0.009912644
## [277,] -0.81536975 0.06801914 -1.185699492 0.327104985 -0.222383394
## [278,] 1.82664376 -1.94248346 1.227017426 1.036361030 0.351287630
## [279,] 1.13742284 -1.16062133 0.919011011 2.738575540 -0.307371694
## [280,] 0.33333178 -0.71384298 0.251663778 2.171170703 -0.009912644
## [281,] 1.76920868 -1.49570510 0.867676609 0.752658612 -0.349865844
## [282,] -0.35588914 -1.43985781 2.099702269 0.681733008 5.748044671
## [283,] -0.41332422 -0.04367545 -0.775024272 0.327104985 -0.434854143
## [284,] 0.33333178 -0.71384298 0.251663778 2.171170703 -0.009912644
## [285,] 0.90768254 0.34725561 -1.031696285 0.610807403 0.308793480
## [286,] 0.90768254 0.34725561 -1.031696285 0.610807403 0.308793480
## [287,] 2.11381914 -0.43460651 1.432355036 -0.382151061 -0.307371694
## [288,] -0.47075929 -0.71384298 -0.775024272 0.327104985 0.096322731
## [289,] 0.21846162 -0.04367545 -0.929027480 -0.027523038 0.075075656
## [290,] 1.88407884 -0.60214839 1.329686231 0.539881799 0.372534705
## [291,] 0.21846162 -0.04367545 -0.929027480 -0.027523038 0.075075656
## [292,] 1.53946838 -1.83078887 1.073014219 -0.382151061 5.429338547
## [293,] 1.19485792 0.12386643 -0.210345844 0.114328171 0.075075656
## [294,] -0.81536975 -0.93723216 -0.107677039 -0.098448642 0.223805180
## [295,] 2.86047514 -1.04892675 1.278351829 0.468956194 0.138816881
## [296,] 1.42459823 -0.15537004 0.970345414 -0.027523038 -0.307371694
## [297,] 1.30972807 1.68759068 0.508335791 0.043402567 -0.031159719
## [298,] -0.70049960 0.57064479 -1.083030687 -0.382151061 -0.094900944
## [299,] -0.64306452 0.68233938 -1.288368298 -0.169374247 0.138816881
## [300,] -0.81536975 0.79403397 -1.083030687 -0.311225456 -0.158642169
## [301,] -0.47075929 0.01217185 -1.083030687 0.043402567 -0.031159719
## [302,] 1.59690346 -1.94248346 1.073014219 -0.736779083 -0.413607068
## [303,] -0.01127868 0.98949950 -0.775024272 0.043402567 0.096322731
## [304,] -0.52819437 0.79403397 -0.775024272 -0.665853479 2.093547777
## [305,] 0.04615639 0.68233938 1.689027049 -0.311225456 0.521264230
## [306,] 1.13742284 0.01217185 1.073014219 -0.027523038 -0.519842443
## [307,] -0.41332422 0.51479750 0.251663778 -0.240299852 -0.116148019
## [308,] 1.13742284 -0.65799569 0.765007803 -0.098448642 2.667218801
## [309,] 1.13742284 -0.54630110 0.867676609 -0.098448642 2.688465876
## [310,] -0.52819437 -1.32816322 0.559670193 -0.594927874 -0.541089518
## [311,] 1.13742284 0.01217185 1.073014219 -0.027523038 -0.519842443
## [312,] -0.24101899 0.01217185 -0.159011442 -0.382151061 -0.328618769
## [313,] 0.39076685 -0.37875921 0.200329376 0.185253776 0.117569806
## [314,] 0.16102655 -0.32291192 0.148994973 0.327104985 -0.243630469
## [315,] -0.52819437 -0.93723216 0.097660571 0.043402567 -0.009912644
## [316,] -0.70049960 -0.99307945 0.097660571 -0.027523038 0.181311031
## [317,] 0.73537731 0.17971373 -0.210345844 0.610807403 0.308793480
## [318,] 0.73537731 1.35250691 -0.775024272 0.256179380 -0.116148019
## [319,] 0.85024746 0.73818667 0.611004596 0.468956194 -0.094900944
## [320,] 0.73537731 1.35250691 -0.775024272 0.256179380 -0.116148019
## [321,] 0.85024746 0.73818667 0.611004596 0.468956194 -0.094900944
## [322,] 0.56307208 0.45895020 -0.056342637 0.610807403 0.053828581
## [323,] -0.29845406 0.51479750 -1.134365090 -0.169374247 -0.179889244
## [324,] 1.13742284 0.34725561 0.765007803 0.185253776 0.053828581
## [325,] 0.96511762 -0.21121733 -0.364349052 6.001153349 -0.349865844
## [326,] 0.96511762 -0.21121733 -0.364349052 6.001153349 -0.349865844
## [327,] 1.88407884 0.01217185 1.997033464 0.788121414 0.712487905
## [328,] 1.13742284 -0.49045380 1.175683024 1.390989053 0.415028855
## [329,] 2.91791021 -1.43985781 1.791695854 0.043402567 -0.116148019
## [330,] 1.36716315 -0.37875921 0.611004596 -0.382151061 -0.562336593
## [331,] 1.07998777 -0.93723216 1.894364659 0.256179380 0.733734980
## [332,] 1.07998777 -0.93723216 1.894364659 0.256179380 0.733734980
## [333,] -0.18358391 0.29140832 0.046326168 0.468956194 -0.456101218
## [334,] 0.04615639 0.17971373 -0.980361882 -0.311225456 0.372534705
## [335,] -0.24101899 0.68233938 -1.339702700 -0.027523038 -0.201136319
## [336,] 2.05638407 0.93365220 1.329686231 0.610807403 0.861217429
## [337,] 0.33333178 -0.54630110 0.919011011 -0.453076665 -0.753560268
## [338,] -0.29845406 -0.54630110 0.251663778 0.185253776 -0.158642169
## [339,] 2.34355945 -0.21121733 1.586358244 0.327104985 0.330040555
## [340,] 2.40099452 -1.38401051 1.381020634 -0.169374247 -0.116148019
## [341,] 2.22868930 -1.04892675 1.175683024 -0.098448642 -0.456101218
## [342,] 1.30972807 -0.60214839 1.073014219 0.114328171 -0.477348293
## [343,] 1.48203330 -0.76969027 1.021679816 -0.524002270 0.648746680
## [344,] 1.48203330 -0.76969027 1.021679816 -0.524002270 0.648746680
## [345,] 2.05638407 0.23556102 1.175683024 0.043402567 -0.116148019
## [346,] -0.75793468 0.87780491 -1.391037103 -0.453076665 -0.434854143
## [347,] -0.98767498 1.60381973 -1.288368298 0.114328171 -0.328618769
## [348,] 3.14765052 -0.21121733 2.048367867 0.327104985 0.117569806
## [349,] 0.73537731 0.17971373 0.200329376 0.185253776 0.032581506
## [350,] 0.44820193 1.43627785 -1.391037103 0.043402567 0.117569806
## [351,] 1.36716315 0.79403397 -0.261680247 0.114328171 0.415028855
## [352,] 0.44820193 1.49212515 -1.391037103 0.043402567 0.181311031
## [353,] -0.35588914 0.76611032 -1.391037103 -0.098448642 0.053828581
## [354,] 2.97534529 0.01217185 2.664380697 1.603765867 0.691240830
## [355,] -1.27485036 -1.77494157 0.662338998 -0.807704688 -0.456101218
## [356,] -0.93023990 1.24081232 -1.339702700 -0.098448642 -0.201136319
## [357,] 1.82664376 -0.65799569 1.278351829 0.327104985 -0.158642169
## [358,] 1.25229300 -0.60214839 1.997033464 0.291642183 0.606252530
## [359,] 2.05638407 -0.54630110 1.997033464 0.398030589 0.457523005
## [360,] 2.45842960 -0.82553757 1.997033464 0.043402567 0.011334431
## [361,] -0.06871376 0.96157585 -0.210345844 -0.382151061 0.245052255
## [362,] 0.16102655 -0.43460651 0.200329376 0.043402567 -0.031159719
## [363,] 2.05638407 0.29140832 1.997033464 -0.027523038 -0.328618769
## [364,] 2.40099452 -0.37875921 1.843030257 -0.382151061 -0.349865844
## [365,] 2.57329975 0.48687385 1.997033464 2.313021912 -0.094900944
## [366,] 0.96511762 -0.60214839 1.175683024 0.610807403 0.415028855
## [367,] 2.57329975 0.48687385 1.997033464 2.313021912 -0.094900944
## [368,] 1.19485792 0.26348467 1.740361451 0.043402567 -0.243630469
## [369,] 1.13742284 -1.04892675 1.278351829 0.185253776 1.519876753
## [370,] 0.62050716 -1.43985781 1.329686231 -0.098448642 -0.286124619
## [371,] -0.81536975 1.32458326 -1.288368298 -0.169374247 -0.519842443
## [372,] -0.24101899 -1.60739969 0.662338998 -0.665853479 -0.668571968
## [373,] 0.44820193 -1.38401051 1.073014219 -0.524002270 -0.434854143
## [374,] -0.52819437 0.12386643 -0.261680247 -0.240299852 0.393781780
## [375,] 3.26252067 -0.65799569 1.843030257 0.894509821 0.032581506
## [376,] 1.82664376 0.06801914 2.253705477 1.320063449 0.776229130
## [377,] 1.82664376 -0.43460651 1.175683024 0.327104985 -0.201136319
## [378,] 0.62050716 -1.43985781 1.329686231 -0.098448642 -0.286124619
## [379,] 1.76920868 0.54272114 1.997033464 2.596724331 0.011334431
## [380,] -0.01127868 -0.60214839 0.559670193 -0.027523038 0.138816881
## [381,] -0.01127868 -1.49570510 0.765007803 -0.382151061 -0.158642169
## [382,] 3.09021544 -0.63007204 2.099702269 0.256179380 -0.052406794
## [383,] -0.01127868 -1.49570510 0.765007803 -0.382151061 -0.158642169
## [384,] -0.01127868 -1.49570510 0.765007803 -0.382151061 -0.158642169
## [385,] -0.35588914 -0.09952274 0.046326168 -0.311225456 -0.009912644
## [386,] -0.52819437 0.57064479 -1.031696285 -0.098448642 0.053828581
## [387,] -0.29845406 0.06801914 -0.056342637 -0.382151061 0.011334431
## [388,] -0.01127868 0.73818667 -0.621021065 -0.453076665 -0.179889244
## [389,] -0.29845406 -0.37875921 -0.056342637 -0.453076665 0.011334431
## [390,] 0.73537731 -0.82553757 0.200329376 -0.027523038 0.181311031
## [391,] -1.56202574 1.79928526 -1.134365090 -0.807704688 -0.902289793
## [392,] 3.09021544 -0.63007204 2.099702269 0.256179380 -0.052406794
## [393,] 0.67794223 -0.88138486 1.278351829 -0.382151061 -0.116148019
## [394,] 0.04615639 0.76611032 1.740361451 -0.382151061 0.521264230
## [395,] 2.51586468 0.40310291 1.945699062 -0.169374247 -0.519842443
## [396,] 2.11381914 -0.88138486 2.510377489 1.178212240 -0.456101218
## [397,] -0.98767498 1.15704138 -1.288368298 3.802459608 0.733734980
## [398,] 1.82664376 0.34725561 1.637692646 0.043402567 -0.009912644
## [399,] 1.82664376 0.34725561 1.637692646 0.043402567 -0.009912644
## [400,] 0.21846162 1.32458326 -0.261680247 -0.169374247 -0.498595368
## [401,] -0.98767498 1.15704138 -1.288368298 3.802459608 0.733734980
## [402,] -0.35588914 -1.49570510 0.148994973 -0.594927874 -0.604830743
## [403,] 2.22868930 -0.26706463 1.381020634 0.043402567 -0.052406794
## [404,] 1.76920868 0.40310291 1.124348621 0.114328171 -0.052406794
## [405,] -0.35588914 0.90572856 -1.134365090 0.114328171 -0.264877544
## [406,] 0.21846162 -1.21646863 0.970345414 -0.807704688 -0.604830743
## [407,] 0.85024746 -0.49045380 1.021679816 -0.027523038 -0.519842443
## [408,] 2.11381914 -0.76969027 1.997033464 0.327104985 0.117569806
## [409,] 1.19485792 -1.04892675 1.586358244 0.823584217 1.838582878
## [410,] 2.40099452 -0.37875921 1.124348621 1.390989053 -0.371112918
## [411,] 0.39076685 -0.54630110 0.354332583 -0.027523038 -0.158642169
## [412,] 0.44820193 -0.43460651 0.405666986 -0.098448642 -0.158642169
## [413,] -0.70049960 1.15704138 -0.569686662 -0.453076665 0.266299330
## [414,] 0.90768254 -0.71384298 1.329686231 2.951352353 0.202558106
## [415,] 0.27589670 -0.04367545 0.354332583 0.114328171 -0.009912644
## [416,] 0.16102655 1.10119409 -0.159011442 2.880426749 0.627499605
## [417,] 1.30972807 -0.26706463 1.894364659 -0.240299852 0.500017155
## [418,] -0.75793468 0.29140832 -0.775024272 -0.453076665 0.075075656
## [419,] 2.05638407 -0.82553757 1.227017426 -0.382151061 0.712487905
## [420,] -0.87280483 1.35250691 -1.391037103 -0.524002270 -0.456101218
## [421,] 0.67794223 0.17971373 0.302998181 -0.098448642 0.032581506
## [422,] -0.98767498 1.74343797 -1.237033895 -0.169374247 -0.604830743
## [423,] -0.35588914 2.41360550 -0.364349052 -0.382151061 -0.859795643
## [424,] 1.25229300 -1.60739969 1.021679816 -0.311225456 -0.456101218
## [425,] -0.35588914 2.41360550 -0.364349052 -0.382151061 -0.859795643
## [426,] -0.98767498 1.74343797 -1.237033895 -0.169374247 -0.604830743
## [427,] -1.10254513 0.79403397 -0.980361882 -0.311225456 -0.902289793
## [428,] 0.67794223 1.40835420 -0.261680247 -0.453076665 -0.222383394
## [429,] 0.44820193 -0.04367545 0.302998181 -0.878630293 -0.371112918
## [430,] 2.57329975 1.74343797 1.843030257 -0.098448642 0.011334431
## [431,] 1.25229300 -1.60739969 1.021679816 -0.311225456 -0.456101218
## [432,] -0.29845406 0.12386643 0.405666986 -0.240299852 -0.286124619
## [433,] 2.05638407 -0.88138486 2.151036672 -0.169374247 -0.201136319
## [434,] 2.28612437 -0.76969027 1.843030257 -0.169374247 0.075075656
## [435,] 1.19485792 -0.65799569 1.432355036 0.468956194 -0.243630469
## [436,] 2.28612437 -0.76969027 1.843030257 -0.169374247 0.075075656
## [437,] -0.18358391 0.79403397 0.148994973 -0.382151061 -0.583583668
## [438,] 1.59690346 -0.43460651 2.356374282 0.468956194 -0.456101218
## [439,] 1.19485792 -0.65799569 1.432355036 0.468956194 -0.243630469
## [440,] -0.75793468 0.51479750 -0.467017857 -0.736779083 -0.541089518
## [441,] 2.45842960 -1.21646863 2.305039879 -0.240299852 -0.328618769
## [442,] 2.05638407 -0.71384298 1.945699062 -0.275762654 -0.413607068
## [443,] 4.18148189 0.87780491 2.510377489 0.823584217 0.266299330
## [444,] 0.96511762 -0.49045380 1.124348621 0.114328171 -0.222383394
## [445,] -1.73433097 0.23556102 -1.339702700 -0.594927874 -0.711066118
## [446,] 0.67794223 1.15704138 -0.877693077 -0.311225456 -0.179889244
## [447,] 2.40099452 -0.82553757 1.689027049 0.043402567 -0.137395094
## [448,] 0.56307208 -0.26706463 0.097660571 -0.311225456 0.839970354
## [449,] 0.16102655 0.01217185 -0.261680247 -0.382151061 0.266299330
## [450,] 2.05638407 -0.76969027 2.151036672 0.185253776 0.160063956
## [451,] 2.05638407 -0.76969027 2.151036672 0.185253776 0.160063956
## [452,] 0.04615639 -0.88138486 1.329686231 -0.524002270 6.916633794
## [453,] -0.87280483 0.17971373 -1.237033895 -0.594927874 -0.073653869
## [454,] 1.19485792 -1.10477404 1.843030257 0.185253776 -0.073653869
## [455,] -0.75793468 -1.66324698 0.662338998 -0.665853479 -0.519842443
## [456,] 1.71177361 0.51479750 2.048367867 1.887468285 -0.031159719
## [457,] 0.33333178 0.34725561 0.611004596 -0.169374247 0.160063956
## [458,] 0.50563700 0.57064479 -0.313014650 0.114328171 0.202558106
## [459,] 1.19485792 -1.10477404 1.843030257 0.185253776 -0.073653869
## [460,] 1.88407884 0.29140832 1.997033464 -0.240299852 -0.286124619
## [461,] 0.50563700 -0.54630110 1.278351829 -0.169374247 -0.094900944
## [462,] -0.01127868 0.48687385 -0.261680247 0.043402567 -0.009912644
## [463,] 1.53946838 -1.49570510 2.099702269 0.007939764 -0.052406794
## [464,] -0.12614884 0.73818667 2.202371074 -0.240299852 0.223805180
## [465,] 1.82664376 -1.18854498 1.381020634 -0.311225456 -0.073653869
## [466,] 0.96511762 -1.32816322 0.662338998 0.256179380 0.223805180
## [467,] 1.13742284 -0.15537004 0.765007803 -0.382151061 -0.392359993
## [468,] 0.27589670 -0.37875921 0.919011011 0.043402567 -0.477348293
## [469,] 1.76920868 -0.93723216 2.151036672 -0.311225456 0.053828581
## [470,] 0.21846162 1.63174338 -1.288368298 -0.949555897 -0.371112918
## [471,] 2.68816991 -1.16062133 1.945699062 0.043402567 0.117569806
## [472,] 0.73537731 0.06801914 0.765007803 -0.098448642 -0.137395094
## [473,] 2.40099452 -0.88138486 1.432355036 0.043402567 -0.094900944
## [474,] 0.90768254 -0.99307945 1.432355036 -0.311225456 -0.541089518
## [475,] 1.25229300 -1.38401051 1.227017426 -0.594927874 -0.158642169
## [476,] 0.73537731 0.84988126 -0.159011442 -0.240299852 -0.009912644
## [477,] 0.56307208 -1.43985781 0.713673401 -0.382151061 0.075075656
## [478,] 1.19485792 -1.60739969 1.124348621 -0.524002270 -0.264877544
## [479,] 0.73537731 0.84988126 -0.159011442 -0.240299852 -0.009912644
## [480,] 0.62050716 0.87780491 -0.826358675 0.114328171 -0.222383394
## [481,] 1.30972807 -1.38401051 0.611004596 9.192805554 -0.392359993
## [482,] 0.62050716 -1.27231592 1.483689439 0.185253776 -0.158642169
## [483,] 1.30972807 -0.93723216 1.637692646 -0.240299852 1.371147229
## [484,] 1.30972807 -0.93723216 1.689027049 -0.240299852 1.371147229
## [485,] 1.30972807 -0.49045380 2.099702269 1.107286635 0.563758380
## [486,] 1.07998777 0.79403397 0.611004596 -0.453076665 -0.711066118
## [487,] 1.07998777 0.79403397 0.611004596 -0.453076665 -0.711066118
## [488,] 1.07998777 0.65441573 0.457001388 -0.524002270 -0.732313193
## [489,] 1.88407884 -1.16062133 1.432355036 0.185253776 -0.137395094
## [490,] 0.56307208 -0.76969027 0.662338998 0.043402567 -0.307371694
## [491,] 0.56307208 1.38043056 -0.005008234 0.185253776 -0.201136319
## [492,] 0.50563700 -0.65799569 1.175683024 -0.027523038 -0.689819043
## [493,] 0.33333178 -0.71384298 1.227017426 0.043402567 -0.753560268
## [494,] 0.21846162 0.90572856 0.200329376 0.327104985 -0.031159719
## [495,] -1.04511006 -0.76969027 -0.210345844 4.086162026 -0.774807343
## [496,] 1.36716315 -0.99307945 1.329686231 0.043402567 -0.371112918
## [497,] -0.29845406 -0.04367545 -0.107677039 -0.453076665 -0.137395094
## [498,] -0.64306452 -1.04892675 0.251663778 -0.027523038 0.053828581
## [499,] 1.36716315 -0.99307945 1.329686231 0.043402567 -0.371112918
## [500,] 0.21846162 0.90572856 0.200329376 0.327104985 -0.031159719
## [501,] -0.29845406 -0.04367545 -0.107677039 -0.453076665 -0.137395094
## [502,] 1.19485792 -0.49045380 2.356374282 2.844963947 -0.286124619
## [503,] 1.19485792 -0.49045380 2.356374282 2.844963947 -0.286124619
## [504,] 1.25229300 -1.49570510 1.021679816 -0.453076665 -0.201136319
## [505,] 1.25229300 -1.60739969 0.765007803 -0.524002270 -0.222383394
## [506,] 1.07998777 -0.21121733 1.843030257 0.256179380 -0.328618769
## [507,] 1.19485792 -1.60739969 0.970345414 -0.524002270 -0.264877544
## [508,] 1.65433853 0.79403397 1.432355036 -0.169374247 -0.073653869
## [509,] 0.96511762 0.34725561 0.200329376 -0.240299852 0.053828581
## [510,] 2.86047514 -1.32816322 2.459043087 0.185253776 -0.073653869
## [511,] 2.34355945 -0.60214839 1.124348621 1.461914658 -0.307371694
## [512,] 0.96511762 0.34725561 0.200329376 -0.240299852 0.053828581
## [513,] 1.36716315 -0.71384298 1.073014219 -0.311225456 0.797476205
## [514,] 1.25229300 -0.09952274 1.894364659 -0.098448642 0.415028855
## [515,] 1.25229300 -0.09952274 1.894364659 -0.098448642 0.415028855
## [516,] 0.10359147 0.71026303 1.124348621 2.525798726 0.733734980
## [517,] 2.40099452 0.40310291 1.124348621 1.249137844 0.266299330
## [518,] 1.19485792 0.45895020 1.124348621 -0.311225456 2.391006827
## [519,] 1.48203330 -1.77494157 1.124348621 0.185253776 0.011334431
## [520,] -0.58562945 -0.90930851 1.124348621 -0.027523038 0.011334431
## [521,] 0.85024746 -1.55155239 1.124348621 0.114328171 0.011334431
## [522,] -0.41332422 -0.65799569 1.124348621 -0.382151061 0.011334431
## [523,] -0.06871376 -0.76969027 1.124348621 -0.169374247 0.245052255
## [524,] 0.56307208 -0.71384298 1.124348621 -0.027523038 -0.052406794
## [525,] 0.50563700 -0.54630110 1.124348621 -0.098448642 -0.031159719
## [526,] 1.19485792 0.62649208 -0.159011442 0.185253776 0.372534705
## [527,] -0.58562945 -0.90930851 1.124348621 -0.027523038 0.011334431
## [528,] -0.75793468 -0.82553757 1.124348621 -0.027523038 0.202558106
## [529,] -0.06871376 -0.60214839 1.124348621 0.043402567 -0.073653869
## [530,] 0.90768254 0.57064479 -0.159011442 -0.098448642 -0.222383394
## [531,] 0.44820193 -1.71909428 -0.159011442 -0.311225456 -0.201136319
## [532,] 2.05638407 -0.82553757 1.124348621 0.114328171 0.223805180
## [533,] 2.05638407 -0.82553757 1.124348621 0.114328171 0.223805180
## [534,] 1.13742284 -1.43985781 -0.159011442 -0.311225456 -0.328618769
## [535,] 0.96511762 -0.26706463 -0.159011442 0.114328171 0.308793480
## [536,] 0.44820193 -1.71909428 -0.159011442 -0.311225456 -0.201136319
## [537,] 0.90768254 0.57064479 -0.159011442 -0.098448642 -0.222383394
## [538,] -0.12614884 1.65966703 -0.159011442 -0.311225456 -0.073653869
## [539,] 2.63073483 -0.99307945 1.124348621 2.313021912 -0.456101218
## [540,] 1.65433853 -0.15537004 2.407708684 1.852005483 0.266299330
## [541,] 0.50563700 0.34725561 -0.159011442 0.539881799 0.287546405
## [542,] 0.67794223 -0.37875921 1.124348621 2.667649935 -0.498595368
## [543,] 0.56307208 1.04534679 -0.159011442 -0.311225456 -0.371112918
## [544,] 1.65433853 0.73818667 -0.159011442 -0.027523038 -0.052406794
## [545,] 3.43482590 -1.21646863 2.407708684 -0.524002270 -0.264877544
## [546,] 0.44820193 -0.32291192 1.124348621 0.043402567 0.138816881
## [547,] -0.47075929 0.12386643 -0.159011442 -0.382151061 -0.201136319
## [548,] 1.30972807 -1.21646863 1.124348621 -0.027523038 -0.434854143
## [549,] 2.34355945 -0.99307945 1.124348621 0.043402567 -0.179889244
## [550,] 0.39076685 0.01217185 1.124348621 -0.453076665 1.774841653
## [551,] -0.87280483 -0.09952274 -1.339702700 -0.311225456 -0.286124619
## [552,] 0.62050716 -0.54630110 -0.159011442 0.185253776 0.096322731
## [553,] 0.67794223 -0.37875921 -0.159011442 0.114328171 0.096322731
## [554,] -1.90663620 2.86038386 -0.159011442 -0.665853479 -0.796054418
## [555,] 4.12404682 0.65441573 1.124348621 1.178212240 0.160063956
## [556,] 4.12404682 0.65441573 1.124348621 1.178212240 0.160063956
## [557,] 1.48203330 0.01217185 1.124348621 1.461914658 0.648746680
## [558,] 4.18148189 0.65441573 1.124348621 1.178212240 0.160063956
## [559,] 1.48203330 0.01217185 1.124348621 1.461914658 0.648746680
## [560,] 2.68816991 -0.32291192 1.124348621 1.249137844 -0.052406794
## [561,] 2.51586468 0.40310291 1.124348621 0.185253776 -0.264877544
## [562,] 0.39076685 -0.49045380 1.124348621 -0.098448642 -0.201136319
## [563,] 0.39076685 0.06801914 1.124348621 0.256179380 0.138816881
## [564,] -0.41332422 -1.32816322 1.124348621 0.114328171 0.096322731
## [565,] 2.68816991 -0.32291192 1.124348621 1.249137844 -0.052406794
## [566,] 2.51586468 0.40310291 1.124348621 0.185253776 -0.264877544
## [567,] 0.21846162 0.96157585 -0.159011442 -0.027523038 2.943430776
## [568,] 0.21846162 0.96157585 -0.159011442 -0.027523038 2.943430776
## [569,] 0.85024746 -0.15537004 1.124348621 0.043402567 3.453360575
## [570,] -1.21741529 -0.93723216 -0.159011442 -0.240299852 0.160063956
## [571,] 1.82664376 -0.99307945 1.124348621 0.539881799 -0.371112918
## [572,] -1.21741529 -0.93723216 -0.159011442 -0.240299852 0.160063956
## [573,] 1.07998777 -1.60739969 1.124348621 -0.098448642 -0.264877544
## [574,] 1.25229300 0.34725561 1.124348621 -0.311225456 -0.371112918
## [575,] 1.30972807 -1.04892675 1.124348621 0.468956194 -0.201136319
## [576,] 2.28612437 -1.43985781 1.124348621 0.398030589 -0.179889244
## [577,] 0.90768254 -0.15537004 -0.159011442 -0.169374247 0.330040555
## [578,] 0.27589670 -0.49045380 1.124348621 0.185253776 -0.094900944
## [579,] 0.27589670 -0.32291192 1.124348621 0.256179380 -0.052406794
## [580,] 1.30972807 -1.21646863 1.124348621 -0.240299852 -0.519842443
## [581,] 2.28612437 -0.15537004 1.124348621 -0.240299852 0.032581506
## [582,] 2.28612437 -0.15537004 1.124348621 -0.240299852 0.032581506
## [583,] 1.94151391 -0.21121733 1.124348621 -0.240299852 -0.094900944
## [584,] 2.11381914 -1.38401051 1.124348621 -0.453076665 -0.286124619
## [585,] 1.99894899 -1.10477404 1.124348621 0.610807403 0.117569806
## [586,] -0.41332422 -0.09952274 -0.159011442 -0.098448642 0.075075656
## [587,] 1.59690346 -1.21646863 1.124348621 0.114328171 0.138816881
## [588,] -0.58562945 1.12911773 -0.159011442 -0.453076665 0.436275930
## [589,] -1.90663620 -0.60214839 -0.159011442 -0.382151061 -0.583583668
## [590,] 1.07998777 -1.32816322 1.124348621 0.043402567 -0.604830743
## [591,] 0.39076685 -0.43460651 1.124348621 0.043402567 -0.073653869
## [592,] -0.98767498 -0.76969027 1.124348621 -0.594927874 -0.371112918
## [593,] 0.39076685 -0.43460651 1.124348621 0.043402567 -0.073653869
## [594,] 0.90768254 -0.21121733 1.586358244 0.681733008 0.138816881
## [595,] -0.24101899 1.07327044 -0.518352260 0.043402567 0.181311031
## [596,] 0.33333178 0.37517926 0.713673401 3.802459608 -0.031159719
## [597,] 2.34355945 -0.71384298 1.227017426 -0.382151061 -0.604830743
## [598,] 2.05638407 0.29140832 1.586358244 -0.453076665 -0.349865844
## [599,] 0.10359147 0.31933196 -0.467017857 -0.311225456 -0.201136319
## [600,] 2.51586468 0.34725561 0.919011011 -0.169374247 -0.116148019
## [601,] -0.06871376 2.16229268 -0.005008234 -0.311225456 0.011334431
## [602,] 2.80304006 -0.37875921 1.278351829 -0.240299852 -0.349865844
## [603,] -0.35588914 1.71551432 -1.391037103 0.043402567 -0.137395094
## [604,] 2.80304006 -0.37875921 1.278351829 -0.240299852 -0.349865844
## [605,] -0.01127868 0.29140832 -0.723689870 0.256179380 0.181311031
## [606,] -0.01127868 0.40310291 -0.723689870 0.043402567 -0.052406794
## [607,] 0.62050716 -0.65799569 1.073014219 1.461914658 -0.328618769
## [608,] 0.27589670 -0.26706463 0.713673401 0.539881799 0.096322731
## [609,] 1.02255269 0.68233938 0.508335791 1.816542681 0.478770080
## [610,] -1.15998021 -0.93723216 -0.415683455 0.468956194 -0.264877544
## [611,] 0.27589670 -1.60739969 1.381020634 -0.027523038 -0.094900944
## [612,] 2.80304006 -0.82553757 1.432355036 0.114328171 -0.137395094
## [613,] -0.47075929 0.62649208 -1.391037103 -0.098448642 -0.222383394
## [614,] -0.06871376 -0.76969027 0.559670193 -0.736779083 -0.626077818
## [615,] 0.50563700 1.26873597 -0.467017857 -0.240299852 1.286158929
## [616,] 0.73537731 0.40310291 1.175683024 -0.169374247 -0.179889244
## [617,] 0.73537731 0.40310291 1.175683024 -0.169374247 -0.179889244
## [618,] 1.82664376 -1.21646863 1.227017426 -0.240299852 -0.179889244
## [619,] 1.76920868 -0.37875921 1.175683024 0.114328171 0.733734980
## [620,] 1.71177361 -0.88138486 0.713673401 -0.169374247 0.011334431
## [621,] -0.01127868 0.06801914 -0.159011442 0.610807403 -0.243630469
## [622,] -0.06871376 0.17971373 -0.210345844 0.610807403 -0.201136319
## [623,] 0.96511762 0.29140832 -0.261680247 -0.453076665 -0.158642169
## [624,] -0.24101899 -0.09952274 -0.107677039 0.256179380 -0.222383394
## [625,] -0.87280483 0.90572856 -1.391037103 2.171170703 0.776229130
## [626,] -0.87280483 0.90572856 -1.391037103 2.171170703 0.776229130
## [627,] 0.27589670 0.40310291 0.097660571 -0.240299852 0.223805180
## [628,] 0.27589670 0.40310291 0.097660571 -0.240299852 0.223805180
## [629,] 0.21846162 0.06801914 -0.056342637 -0.027523038 0.202558106
## [630,] -0.41332422 0.87780491 -0.210345844 -0.169374247 0.500017155
## [631,] 0.21846162 0.06801914 -0.056342637 -0.027523038 0.202558106
## [632,] 1.19485792 -1.38401051 1.381020634 0.114328171 0.372534705
## [633,] -0.41332422 -0.65799569 -0.672355467 0.327104985 -0.009912644
## [634,] 1.02255269 2.27398727 -0.261680247 0.610807403 0.372534705
## [635,] -0.24101899 -0.99307945 -0.313014650 -0.453076665 -0.307371694
## [636,] 0.21846162 1.74343797 -1.391037103 -0.807704688 -0.477348293
## [637,] 0.73537731 1.96682715 0.046326168 -0.098448642 -0.031159719
## [638,] 0.67794223 1.99475079 -0.005008234 -0.169374247 -0.073653869
## [639,] -0.35588914 2.16229268 -0.775024272 -0.240299852 1.179923554
## [640,] 0.33333178 -1.32816322 0.405666986 -0.453076665 -0.434854143
## [641,] 0.90768254 0.06801914 0.919011011 -0.169374247 -0.349865844
## [642,] 0.67794223 0.34725561 0.867676609 -0.169374247 -0.349865844
## [643,] 0.90768254 0.06801914 0.919011011 -0.169374247 -0.349865844
## [644,] 0.67794223 0.34725561 0.867676609 -0.169374247 -0.349865844
## [645,] 0.90768254 0.06801914 0.919011011 -0.169374247 -0.349865844
## [646,] -0.29845406 0.62649208 -0.877693077 2.454873122 0.585005455
## [647,] -0.58562945 0.79403397 -1.134365090 0.752658612 0.415028855
## [648,] -0.01127868 1.77136162 -1.339702700 -0.240299852 -0.371112918
## [649,] 0.21846162 -0.26706463 0.148994973 0.185253776 -0.456101218
## [650,] -0.93023990 -0.60214839 -0.005008234 4.298938840 -0.413607068
## [651,] 1.36716315 -0.54630110 0.611004596 -0.240299852 0.393781780
## [652,] 0.85024746 1.96682715 -0.107677039 -0.027523038 0.351287630
## [653,] 4.35378712 -0.93723216 1.945699062 3.518757190 0.181311031
## [654,] 0.62050716 -1.10477404 1.637692646 0.185253776 -0.179889244
## [655,] 0.16102655 -0.32291192 1.021679816 -0.098448642 -0.286124619
## [656,] 0.79281239 0.12386643 -0.518352260 0.256179380 -0.009912644
## [657,] 1.36716315 -0.54630110 0.611004596 -0.240299852 0.393781780
## [658,] 2.11381914 -0.15537004 1.637692646 -0.807704688 -0.307371694
## [659,] -0.64306452 -0.04367545 -1.031696285 -0.807704688 -0.286124619
## [660,] -0.70049960 1.74343797 -1.288368298 1.320063449 0.181311031
## [661,] -0.64306452 -0.04367545 -1.031696285 -0.807704688 -0.286124619
## [662,] -0.47075929 -0.60214839 0.200329376 -0.665853479 -0.158642169
## [663,] -0.64306452 0.23556102 -1.083030687 -0.665853479 -0.243630469
## [664,] 1.02255269 -1.38401051 0.970345414 -0.524002270 -0.796054418
## [665,] 2.17125422 -0.71384298 1.278351829 -0.382151061 0.096322731
## [666,] 0.62050716 0.34725561 -0.672355467 -0.382151061 -0.073653869
## [667,] -0.01127868 -0.21121733 0.457001388 -0.524002270 2.858442476
## [668,] 1.71177361 -1.04892675 0.919011011 -0.382151061 -0.116148019
## [669,] 0.96511762 1.12911773 0.816342206 -0.169374247 -0.604830743
## [670,] 1.71177361 -1.04892675 0.919011011 -0.382151061 -0.116148019
## [671,] -0.81536975 -0.71384298 -0.159011442 -0.027523038 -0.094900944
## [672,] -0.06871376 1.12911773 -0.313014650 -0.594927874 -0.286124619
## [673,] 0.85024746 3.97732974 0.354332583 -0.382151061 -0.179889244
## [674,] -0.06871376 1.12911773 -0.313014650 -0.594927874 -0.286124619
## [675,] 1.42459823 -0.71384298 0.713673401 -0.240299852 -0.073653869
## [676,] 0.56307208 -0.65799569 0.611004596 -0.240299852 -0.498595368
## [677,] 1.42459823 -0.71384298 0.713673401 -0.240299852 -0.073653869
## [678,] 0.16102655 1.52004879 -0.826358675 -0.169374247 -0.073653869
## [679,] -0.01127868 1.40835420 -0.877693077 0.043402567 -0.137395094
## [680,] 1.42459823 -1.49570510 0.919011011 0.539881799 -0.583583668
## [681,] 2.86047514 -0.54630110 1.586358244 -0.453076665 -0.371112918
## [682,] -0.18358391 -0.43460651 -0.210345844 -0.240299852 0.138816881
## [683,] 0.10359147 -0.37875921 0.200329376 -0.204837049 -0.201136319
## [684,] -0.12614884 1.40835420 -0.210345844 0.043402567 -0.604830743
## [685,] 0.85024746 2.52530009 0.251663778 -0.169374247 -0.201136319
## [686,] -0.12614884 1.40835420 -0.210345844 0.043402567 -0.604830743
## [687,] -0.70049960 0.68233938 -0.467017857 -0.524002270 -0.371112918
## [688,] 0.44820193 0.62649208 -0.210345844 0.398030589 0.160063956
## [689,] -0.35588914 0.73818667 -1.185699492 -0.665853479 -1.029772242
## [690,] -0.12614884 -0.82553757 1.073014219 -0.524002270 1.477382603
## [691,] -0.52819437 3.67016962 -1.391037103 1.213675042 0.202558106
## [692,] 0.50563700 2.19021632 -0.159011442 0.043402567 -0.009912644
## [693,] 0.16102655 -0.21121733 1.227017426 -0.382151061 7.107857469
## [694,] 0.39076685 -0.26706463 0.251663778 0.185253776 -0.073653869
## [695,] 0.39076685 -0.32291192 0.200329376 0.114328171 -0.073653869
## [696,] -1.84920113 -0.32291192 -1.288368298 -0.878630293 -1.136007617
## [697,] -0.75793468 0.68233938 -1.288368298 -0.311225456 -0.456101218
## [698,] -0.75793468 0.68233938 -1.288368298 -0.311225456 -0.456101218
## [699,] 0.62050716 0.48687385 0.046326168 0.468956194 -0.009912644
## [700,] 1.99894899 -0.82553757 1.432355036 -0.311225456 -0.349865844
## [701,] 1.30972807 2.74868927 0.816342206 0.256179380 -0.243630469
## [702,] -0.75793468 0.68233938 -1.288368298 -0.311225456 -0.456101218
## [703,] -0.75793468 0.62649208 -1.288368298 -0.311225456 -0.434854143
## [704,] -0.47075929 -0.82553757 1.073014219 0.043402567 -0.307371694
## [705,] 0.44820193 1.32458326 -1.185699492 -0.665853479 -0.201136319
## [706,] 0.04615639 2.83246021 -0.621021065 2.454873122 -0.307371694
## [707,] -0.75793468 1.40835420 -0.980361882 -0.382151061 0.117569806
## [708,] -0.52819437 -0.21121733 -0.415683455 0.327104985 -0.222383394
## [709,] -0.29845406 0.09594279 -0.775024272 -0.027523038 -0.413607068
## [710,] 0.79281239 -1.21646863 1.021679816 -0.665853479 -0.541089518
## [711,] 1.30972807 2.77661292 0.816342206 0.185253776 -0.158642169
## [712,] 0.33333178 0.20763738 0.354332583 0.327104985 0.117569806
## [713,] 0.21846162 0.90572856 -1.391037103 0.468956194 -0.073653869
## [714,] -0.18358391 -0.54630110 0.457001388 -0.169374247 -0.264877544
## [715,] 0.90768254 1.18496503 0.046326168 0.043402567 -0.201136319
## [716,] -0.64306452 -0.21121733 -0.467017857 0.114328171 -0.392359993
## [717,] -0.18358391 -0.54630110 0.457001388 -0.169374247 -0.264877544
## [718,] -0.41332422 -0.37875921 -0.826358675 0.043402567 -0.179889244
## [719,] 0.04615639 0.17971373 -1.185699492 -0.382151061 -0.116148019
## [720,] -0.70049960 0.73818667 -1.391037103 0.965435426 -0.031159719
## [721,] 0.04615639 0.17971373 -1.185699492 -0.382151061 -0.116148019
## [722,] 0.33333178 -0.26706463 -0.159011442 0.220716578 0.138816881
## [723,] -0.41332422 -0.60214839 -0.980361882 0.114328171 -0.073653869
## [724,] -0.70049960 -1.21646863 0.148994973 -0.240299852 -0.732313193
## [725,] -0.47075929 3.27923856 -0.877693077 0.398030589 -0.031159719
## [726,] 0.39076685 0.73818667 -0.518352260 0.327104985 -0.222383394
## [727,] -0.12614884 1.07327044 -0.929027480 0.185253776 -0.073653869
## [728,] -1.10254513 0.23556102 -1.288368298 -0.524002270 -0.434854143
## [729,] -1.10254513 0.23556102 -1.288368298 -0.524002270 -0.434854143
## [730,] -1.10254513 1.88305621 -1.237033895 0.468956194 -0.349865844
## [731,] 0.67794223 0.12386643 1.997033464 -0.169374247 6.364209845
## [732,] 0.33333178 1.93890350 -0.723689870 0.646270205 0.011334431
## [733,] -0.58562945 1.71551432 -1.237033895 -0.311225456 0.096322731
## [734,] -0.75793468 -0.43460651 0.354332583 0.114328171 -0.116148019
## [735,] -0.35588914 0.17971373 -0.364349052 -0.382151061 -0.264877544
## [736,] -0.35588914 2.44152915 -0.877693077 -0.311225456 0.521264230
## [737,] -0.35588914 2.44152915 -0.877693077 -0.311225456 0.521264230
## [738,] -0.06871376 0.34725561 -1.391037103 -0.027523038 0.117569806
## [739,] 0.39076685 -0.37875921 -0.210345844 0.185253776 0.096322731
## [740,] 0.39076685 0.90572856 -1.391037103 -0.098448642 0.011334431
## [741,] -0.01127868 1.29665962 0.097660571 1.178212240 -0.264877544
## [742,] 0.50563700 0.01217185 -0.159011442 0.043402567 -0.201136319
## [743,] -1.04511006 0.48687385 -1.391037103 -0.453076665 -0.477348293
## [744,] 1.88407884 -0.65799569 1.586358244 0.185253776 0.181311031
## [745,] 1.59690346 -0.76969027 1.381020634 0.114328171 0.160063956
## [746,] -0.58562945 -0.09952274 -0.467017857 -0.311225456 -0.371112918
## [747,] -0.06871376 -1.04892675 0.559670193 -0.027523038 -0.158642169
## [748,] 0.16102655 -1.10477404 0.662338998 0.043402567 -0.094900944
## [749,] -0.64306452 -0.15537004 -0.467017857 -0.311225456 -0.349865844
## [750,] -0.58562945 -0.09952274 -0.467017857 -0.311225456 -0.371112918
## [751,] -0.01127868 0.68233938 -0.877693077 0.256179380 0.032581506
## [752,] -0.01127868 0.68233938 -0.877693077 0.256179380 0.032581506
## [753,] -0.41332422 0.06801914 -0.723689870 -0.027523038 0.202558106
## [754,] -0.01127868 0.68233938 -0.877693077 0.256179380 0.032581506
## [755,] -0.29845406 -0.26706463 2.099702269 -0.594927874 6.959127944
## [756,] -0.29845406 2.13436903 -1.031696285 -0.453076665 -0.626077818
## [757,] -1.15998021 2.52530009 -1.339702700 -0.382151061 -0.647324893
## [758,] -0.12614884 1.91097985 -1.391037103 -0.240299852 -0.073653869
## [759,] -0.12614884 1.91097985 -1.391037103 -0.240299852 -0.073653869
## [760,] 0.27589670 -0.60214839 -0.313014650 -0.027523038 0.096322731
## [761,] 0.39076685 0.29140832 -0.107677039 0.185253776 -0.264877544
## [762,] 0.56307208 0.71026303 -0.056342637 -0.382151061 0.181311031
## [763,] 0.27589670 0.96157585 -1.391037103 -0.594927874 -0.392359993
## [764,] 0.56307208 0.71026303 -0.056342637 -0.382151061 0.181311031
## [765,] 0.44820193 0.84988126 -0.826358675 0.185253776 0.117569806
## [766,] 0.50563700 0.79403397 -0.877693077 0.327104985 0.075075656
## [767,] 0.27589670 0.34725561 -0.467017857 0.256179380 0.032581506
## [768,] -0.47075929 0.40310291 0.251663778 0.114328171 0.330040555
## [769,] -0.70049960 0.34725561 -1.288368298 -0.169374247 -0.116148019
## [770,] -0.24101899 1.07327044 -1.339702700 -0.453076665 -0.243630469
## [771,] -0.70049960 0.34725561 -1.288368298 -0.169374247 -0.116148019
## [772,] 0.62050716 0.87780491 -0.056342637 -0.098448642 -0.116148019
## [773,] 0.67794223 0.23556102 -0.005008234 -0.169374247 -0.116148019
## [774,] -0.24101899 -0.71384298 0.097660571 -0.524002270 1.477382603
## [775,] -0.24101899 -0.71384298 0.148994973 -0.524002270 1.477382603
## [776,] -0.64306452 2.63699468 -1.391037103 0.327104985 0.308793480
## [777,] -0.81536975 1.32458326 -0.467017857 -0.098448642 3.304631050
## [778,] -0.81536975 0.59856844 -0.518352260 -0.098448642 3.262136900
## [779,] -0.01127868 -0.54630110 0.148994973 0.610807403 -0.179889244
## [780,] -0.70049960 -0.04367545 -1.237033895 0.043402567 -0.243630469
## [781,] -0.75793468 0.23556102 -1.391037103 -0.382151061 2.178536077
## [782,] -1.04511006 -0.37875921 -0.672355467 -0.098448642 0.563758380
## [783,] 0.39076685 1.63174338 -1.134365090 -0.098448642 -0.137395094
## [784,] -1.04511006 -0.37875921 -0.672355467 -0.098448642 0.563758380
## [785,] -0.70049960 0.34725561 -1.339702700 -0.027523038 -0.222383394
## [786,] 0.90768254 -0.99307945 0.713673401 -0.169374247 -0.094900944
## [787,] 0.90768254 -0.99307945 0.713673401 -0.169374247 -0.094900944
## [788,] 0.96511762 0.17971373 -0.159011442 -0.240299852 -0.179889244
## [789,] 0.96511762 0.17971373 -0.159011442 -0.240299852 -0.179889244
## [790,] 0.16102655 0.57064479 -0.518352260 0.256179380 0.245052255
## [791,] -0.52819437 -0.88138486 0.816342206 0.043402567 -0.116148019
## [792,] 0.27589670 0.62649208 -0.518352260 0.256179380 -0.073653869
## [793,] -0.70049960 0.45895020 -1.288368298 -0.027523038 -0.137395094
## [794,] -0.35588914 0.40310291 -1.391037103 0.043402567 -0.689819043
## [795,] 1.02255269 -1.43985781 1.381020634 -0.169374247 -0.477348293
## [796,] 1.42459823 2.02267444 0.148994973 0.043402567 0.946205729
## [797,] 0.21846162 -0.37875921 0.200329376 -0.027523038 0.818723279
## [798,] 0.56307208 -0.88138486 0.867676609 -0.665853479 -1.051019317
## [799,] 0.62050716 -0.15537004 0.354332583 0.752658612 -0.116148019
## [800,] 0.62050716 -0.15537004 0.354332583 0.752658612 -0.116148019
## [801,] -0.64306452 0.45895020 -0.980361882 1.036361030 -0.116148019
## [802,] 0.16102655 0.12386643 -0.929027480 0.539881799 -0.413607068
## [803,] -1.84920113 0.31933196 -1.391037103 -0.594927874 -0.923536868
## [804,] -0.35588914 0.17971373 -0.980361882 -0.027523038 0.563758380
## [805,] 0.04615639 -0.04367545 -0.261680247 0.114328171 -0.073653869
## [806,] -0.06871376 -1.38401051 0.662338998 -0.098448642 -0.753560268
## [807,] 0.04615639 -1.55155239 0.611004596 -0.382151061 -0.987278092
## [808,] -0.06871376 -1.38401051 0.662338998 -0.098448642 -0.753560268
## [809,] -0.52819437 0.01217185 -0.775024272 -0.453076665 1.647359203
## [810,] -0.41332422 -0.26706463 0.200329376 0.185253776 -0.371112918
## [811,] -0.58562945 -0.21121733 -0.877693077 0.043402567 -0.413607068
## [812,] 2.63073483 -0.15537004 1.432355036 0.185253776 -0.328618769
## [813,] 1.42459823 -0.43460651 0.302998181 -0.027523038 0.245052255
## [814,] -0.81536975 -0.76969027 -0.159011442 -0.311225456 0.308793480
## [815,] 2.45842960 -0.65799569 1.381020634 0.185253776 0.330040555
## [816,] 1.42459823 -0.43460651 0.302998181 -0.027523038 0.245052255
## [817,] 0.85024746 -0.09952274 -0.415683455 0.468956194 -0.137395094
## [818,] 1.42459823 -1.32816322 0.765007803 -0.665853479 -0.073653869
## [819,] -0.70049960 1.04534679 -1.391037103 -0.133911445 -0.349865844
## [820,] 0.44820193 0.73818667 -0.621021065 0.468956194 0.202558106
## [821,] -0.75793468 0.87780491 -1.391037103 -0.453076665 0.245052255
## [822,] -1.96407128 -0.60214839 -1.391037103 -0.311225456 -0.838548568
## [823,] -0.93023990 0.06801914 -0.723689870 -0.382151061 -0.243630469
## [824,] -0.93023990 0.06801914 -0.723689870 -0.382151061 -0.243630469
## [825,] -0.70049960 -0.26706463 0.046326168 0.185253776 -0.413607068
## [826,] -0.70049960 -0.37875921 -0.672355467 0.185253776 -0.243630469
## [827,] -0.47075929 -1.43985781 0.354332583 -0.169374247 -0.796054418
## [828,] -0.70049960 -0.37875921 -0.672355467 0.185253776 -0.243630469
## [829,] -0.29845406 0.23556102 -0.929027480 -0.169374247 -0.477348293
## [830,] -1.38972052 0.45895020 -0.980361882 -0.311225456 -0.349865844
## [831,] -0.47075929 0.87780491 -1.031696285 -0.027523038 -0.626077818
## [832,] -1.38972052 0.45895020 -0.980361882 -0.311225456 -0.349865844
## [833,] 1.19485792 -0.49045380 0.765007803 -0.736779083 1.222417704
## [834,] 1.88407884 -0.32291192 0.867676609 -0.665853479 1.264911854
## [835,] 0.27589670 0.87780491 -0.056342637 -0.665853479 0.011334431
## [836,] -0.41332422 0.76611032 -0.877693077 -0.736779083 -0.456101218
## [837,] -0.93023990 -1.38401051 0.046326168 -0.098448642 -1.603443266
## [838,] -0.93023990 -1.38401051 0.046326168 -0.098448642 -1.603443266
## [839,] 1.02255269 -1.21646863 0.405666986 -0.665853479 -0.264877544
## [840,] -1.33228544 -0.15537004 -1.185699492 -0.240299852 0.096322731
## [841,] 1.59690346 -0.60214839 1.021679816 0.078865369 -0.052406794
## [842,] -0.98767498 0.73818667 -1.391037103 0.327104985 0.585005455
## [843,] 1.30972807 -0.15537004 0.919011011 0.043402567 0.669993755
## [844,] -0.70049960 0.87780491 0.405666986 -0.382151061 0.011334431
## [845,] 0.90768254 -1.55155239 0.970345414 -0.594927874 -0.541089518
## [846,] -1.10254513 0.62649208 -0.313014650 -0.524002270 -0.137395094
## [847,] -1.10254513 0.62649208 -0.313014650 -0.524002270 -0.137395094
## [848,] -0.52819437 0.84988126 -0.569686662 -0.524002270 -0.201136319
## [849,] -1.10254513 0.62649208 -0.313014650 -0.524002270 -0.137395094
## [850,] -1.10254513 0.57064479 -0.313014650 -0.665853479 -0.158642169
## [851,] 0.56307208 -0.54630110 0.867676609 -0.453076665 -0.052406794
## [852,] 0.56307208 -0.54630110 0.867676609 -0.453076665 -0.052406794
## [853,] -0.18358391 -0.60214839 0.251663778 -0.027523038 -0.158642169
## [854,] 0.56307208 -0.93723216 0.611004596 -0.736779083 -0.158642169
## [855,] 0.56307208 -0.93723216 0.611004596 -0.736779083 -0.158642169
## [856,] -0.41332422 1.15704138 -1.288368298 -0.027523038 -0.349865844
## [857,] 0.56307208 -0.93723216 0.611004596 -0.736779083 -0.158642169
## [858,] -0.06871376 -1.49570510 0.354332583 -0.027523038 -0.307371694
## [859,] 1.94151391 -1.38401051 1.021679816 -0.594927874 -0.711066118
## [860,] -0.87280483 0.17971373 -0.261680247 -0.524002270 -0.286124619
## [861,] -0.64306452 0.51479750 -1.083030687 0.114328171 -0.222383394
## [862,] -1.44715559 2.69284197 1.997033464 -0.382151061 -1.029772242
## [863,] -0.47075929 -0.60214839 0.251663778 0.114328171 -0.434854143
## [864,] -0.64306452 0.51479750 -1.083030687 -0.027523038 -0.201136319
## [865,] -0.64306452 0.51479750 -1.083030687 0.114328171 -0.222383394
## [866,] -0.64306452 0.59856844 -1.031696285 0.043402567 -0.222383394
## [867,] -0.87280483 -0.21121733 -0.261680247 -0.169374247 -0.349865844
## [868,] -0.81536975 -0.09952274 -0.210345844 -0.382151061 -0.328618769
## [869,] -0.87280483 0.17971373 -0.261680247 -0.524002270 -0.286124619
## [870,] -0.41332422 0.57064479 -1.237033895 -0.382151061 -0.158642169
## [871,] -0.35588914 1.04534679 -1.339702700 -0.311225456 -0.498595368
## [872,] -0.81536975 0.17971373 -1.237033895 -0.736779083 -0.031159719
## [873,] -0.58562945 -0.99307945 -0.159011442 -0.382151061 -0.434854143
## [874,] 0.44820193 -1.77494157 0.508335791 -0.665853479 -0.434854143
## [875,] 1.19485792 -0.82553757 0.970345414 -0.311225456 0.351287630
## [876,] 0.27589670 -1.21646863 0.662338998 0.185253776 0.457523005
## [877,] -0.70049960 -0.32291192 -1.391037103 -0.240299852 -0.434854143
## [878,] -0.35588914 1.04534679 -1.339702700 -0.311225456 -0.498595368
## [879,] 0.27589670 0.45895020 -0.415683455 1.036361030 0.138816881
## [880,] -0.64306452 0.40310291 -1.185699492 -0.027523038 -0.243630469
## [881,] 0.50563700 0.17971373 -0.467017857 -0.665853479 -0.201136319
## [882,] -0.41332422 1.04534679 -1.391037103 -0.311225456 -0.413607068
## [883,] 0.04615639 -1.21646863 0.097660571 0.398030589 2.263524377
## [884,] -0.64306452 0.40310291 -1.185699492 -0.027523038 -0.243630469
## [885,] 0.27589670 0.45895020 -0.415683455 1.036361030 0.138816881
## [886,] 0.33333178 1.24081232 -0.672355467 -0.027523038 -0.031159719
## [887,] 0.39076685 1.52004879 -0.775024272 -0.098448642 -0.094900944
## [888,] 1.36716315 -0.04367545 0.559670193 0.043402567 -0.456101218
## [889,] -0.87280483 0.23556102 -1.391037103 -0.027523038 -0.328618769
## [890,] 1.36716315 2.07852174 0.354332583 2.880426749 0.521264230
## [891,] -0.64306452 -1.04892675 -0.159011442 -0.382151061 -0.349865844
## [892,] -0.64306452 0.73818667 -1.237033895 -0.169374247 -0.201136319
## [893,] 1.02255269 -0.43460651 -0.210345844 -0.453076665 -0.116148019
## [894,] -0.64306452 0.73818667 -1.237033895 -0.169374247 -0.201136319
## [895,] -0.64306452 0.57064479 -1.237033895 -0.240299852 -0.158642169
## [896,] -0.70049960 0.34725561 -1.339702700 -0.169374247 -0.158642169
## [897,] -0.01127868 -1.21646863 0.611004596 -0.098448642 -0.201136319
## [898,] -0.70049960 0.34725561 -1.339702700 -0.169374247 -0.158642169
## [899,] -0.01127868 -1.21646863 0.611004596 -0.098448642 -0.201136319
## [900,] -0.01127868 2.74868927 -1.288368298 0.610807403 -0.073653869
## [901,] 0.33333178 -1.21646863 0.457001388 0.043402567 -0.668571968
## [902,] -0.52819437 0.59856844 -0.877693077 -0.098448642 -0.158642169
## [903,] -0.52819437 0.59856844 -0.877693077 -0.098448642 -0.158642169
## [904,] -0.87280483 0.34725561 -1.083030687 2.454873122 -0.583583668
## [905,] -0.87280483 0.34725561 -1.083030687 2.454873122 -0.583583668
## [906,] 0.50563700 0.29140832 -0.364349052 0.327104985 -0.137395094
## [907,] -0.64306452 0.06801914 -0.005008234 0.043402567 -0.073653869
## [908,] -1.27485036 0.17971373 -1.391037103 -0.240299852 -0.179889244
## [909,] -0.52819437 -0.04367545 -0.723689870 -0.098448642 -0.201136319
## [910,] -0.58562945 -1.24439228 0.611004596 -0.949555897 -0.604830743
## [911,] 0.56307208 -0.82553757 1.073014219 0.894509821 0.946205729
## [912,] 0.44820193 -1.38401051 0.970345414 4.582641258 0.563758380
## [913,] 0.96511762 -0.37875921 0.867676609 0.256179380 -0.477348293
## [914,] 0.62050716 -0.74176663 0.970345414 1.461914658 0.138816881
## [915,] -0.58562945 -1.24439228 0.611004596 -0.949555897 -0.604830743
## [916,] 0.16102655 -1.18854498 0.662338998 -0.240299852 -0.179889244
## [917,] -1.73433097 1.04534679 -0.415683455 -0.736779083 1.562370903
## [918,] -0.87280483 -0.65799569 0.200329376 4.440790049 -0.073653869
## [919,] 0.04615639 -0.93723216 0.251663778 -0.240299852 -0.137395094
## [920,] 0.04615639 0.51479750 -0.775024272 -0.524002270 -0.328618769
## [921,] 0.73537731 -0.65799569 0.508335791 -0.169374247 0.075075656
## [922,] 0.04615639 -0.93723216 0.251663778 -0.240299852 -0.137395094
## [923,] 0.04615639 0.51479750 -0.775024272 -0.524002270 -0.328618769
## [924,] -0.87280483 -0.65799569 0.200329376 4.440790049 -0.073653869
## [925,] 0.16102655 -0.32291192 -0.005008234 -0.169374247 -0.689819043
## [926,] 0.16102655 -1.71909428 0.457001388 -0.453076665 -0.498595368
## [927,] 0.62050716 -1.60739969 0.302998181 -0.169374247 -0.562336593
## [928,] 0.04615639 0.79403397 -0.415683455 -0.240299852 0.117569806
## [929,] 0.16102655 -0.32291192 -0.005008234 -0.169374247 -0.689819043
## [930,] 0.21846162 -1.10477404 0.559670193 0.539881799 -0.519842443
## [931,] -0.98767498 0.45895020 -1.339702700 -0.453076665 -0.158642169
## [932,] -0.52819437 0.45895020 -1.339702700 -0.382151061 -0.286124619
## [933,] -0.41332422 -0.71384298 0.097660571 -0.453076665 -0.201136319
## [934,] -0.52819437 0.45895020 -1.339702700 -0.382151061 -0.286124619
## [935,] -0.98767498 0.45895020 -1.339702700 -0.453076665 -0.158642169
## [936,] 0.27589670 -1.27231592 0.559670193 -0.169374247 -0.583583668
## [937,] 0.27589670 -1.27231592 0.559670193 -0.169374247 -0.583583668
## [938,] 2.11381914 0.57064479 1.175683024 -0.807704688 -0.349865844
## [939,] -0.64306452 -0.82553757 0.559670193 0.185253776 -0.413607068
## [940,] -1.21741529 -0.37875921 -0.518352260 -0.665853479 -0.307371694
## [941,] 0.73537731 -1.10477404 1.278351829 -0.240299852 -0.286124619
## [942,] 0.90768254 -1.43985781 1.124348621 1.745617076 -0.116148019
## [943,] 1.02255269 -0.54630110 0.662338998 0.043402567 0.096322731
## [944,] 0.85024746 -0.15537004 0.354332583 -0.169374247 0.138816881
## [945,] -0.01127868 -1.27231592 1.124348621 0.894509821 0.053828581
## [946,] 1.07998777 -0.49045380 0.765007803 -0.382151061 -0.349865844
## [947,] 1.07998777 -0.49045380 1.586358244 1.107286635 0.096322731
## [948,] -0.01127868 -1.38401051 1.073014219 -0.311225456 0.117569806
## [949,] 0.33333178 -2.27756722 0.919011011 -0.524002270 -0.264877544
## [950,] 0.33333178 -2.27756722 0.919011011 -0.524002270 -0.264877544
## [951,] 0.33333178 -2.27756722 0.919011011 -0.524002270 -0.264877544
## [952,] -0.01127868 -1.38401051 1.073014219 -0.311225456 0.117569806
## [953,] -0.06871376 -1.21646863 0.662338998 -0.240299852 -0.626077818
## [954,] 1.07998777 -1.04892675 1.073014219 -0.311225456 -0.753560268
## [955,] -0.41332422 -0.54630110 0.662338998 0.114328171 -0.116148019
## [956,] 0.10359147 -1.77494157 1.278351829 -0.453076665 0.053828581
## [957,] 0.39076685 -0.93723216 1.278351829 -0.311225456 0.500017155
## [958,] 0.67794223 -0.88138486 1.278351829 -0.382151061 0.011334431
## [959,] -1.10254513 0.23556102 -0.775024272 -0.169374247 0.691240830
## [960,] -0.18358391 0.34725561 -1.134365090 -0.382151061 0.032581506
## [961,] 0.10359147 -0.32291192 -0.005008234 -0.453076665 -0.626077818
## [962,] -0.70049960 0.17971373 -0.672355467 -0.665853479 -0.201136319
## [963,] -0.98767498 0.23556102 -1.288368298 -0.311225456 0.585005455
## [964,] 0.27589670 -1.43985781 0.611004596 -0.382151061 0.266299330
## [965,] 0.10359147 -0.32291192 -0.005008234 -0.453076665 -0.626077818
## [966,] -0.01127868 -1.04892675 0.662338998 -0.098448642 -0.477348293
## [967,] 0.39076685 -0.82553757 0.713673401 -0.098448642 0.330040555
## [968,] 0.10359147 0.73818667 -0.364349052 -0.311225456 0.202558106
## [969,] 0.39076685 -0.71384298 0.816342206 -0.098448642 -0.413607068
## [970,] -0.93023990 0.17971373 -0.929027480 0.256179380 -0.179889244
## [971,] 1.19485792 -1.49570510 1.073014219 -0.453076665 -0.456101218
## [972,] 1.19485792 -1.49570510 1.073014219 -0.453076665 -0.456101218
## [973,] 1.02255269 -0.82553757 1.175683024 -0.098448642 0.351287630
## [974,] 0.10359147 -1.04892675 0.867676609 -0.594927874 -0.179889244
## [975,] 0.27589670 -1.10477404 0.713673401 2.383947517 -0.307371694
## [976,] -0.64306452 -0.65799569 0.148994973 -0.311225456 -0.094900944
## [977,] -0.64306452 -0.65799569 0.148994973 -0.311225456 -0.094900944
## [978,] 0.04615639 0.34725561 0.097660571 0.043402567 0.457523005
## [979,] -0.75793468 -0.71384298 0.251663778 0.752658612 -0.562336593
## [980,] 2.22868930 -0.43460651 1.124348621 -0.807704688 -0.264877544
## [981,] 0.44820193 -0.15537004 0.148994973 -0.453076665 -0.477348293
## [982,] 0.67794223 1.85513256 -0.056342637 -0.453076665 -0.179889244
## [983,] -0.58562945 -0.04367545 0.251663778 -0.311225456 -0.371112918
## [984,] 0.44820193 -0.15537004 0.148994973 -0.453076665 -0.477348293
## [985,] 2.22868930 -0.43460651 1.124348621 -0.807704688 -0.264877544
## [986,] -0.52819437 0.29140832 -1.391037103 -0.382151061 -0.498595368
## [987,] 0.85024746 -1.04892675 0.611004596 -0.807704688 -0.456101218
## [988,] -0.70049960 -0.93723216 0.148994973 -0.665853479 -0.158642169
## [989,] -0.35588914 -0.76969027 -0.775024272 -0.594927874 0.202558106
## [990,] 0.79281239 -1.30023957 0.662338998 -0.736779083 -0.307371694
## [991,] -0.35588914 -0.76969027 -0.775024272 -0.594927874 0.202558106
## [992,] -0.70049960 -1.04892675 0.046326168 -0.382151061 -0.116148019
## [993,] -1.04511006 -0.71384298 -0.877693077 -0.382151061 -0.243630469
## [994,] -0.70049960 -1.04892675 0.046326168 -0.382151061 -0.116148019
## [995,] 0.96511762 -0.99307945 0.919011011 -0.027523038 0.096322731
## [996,] -0.35588914 0.40310291 -1.083030687 -0.382151061 -0.179889244
## [997,] -1.56202574 0.73818667 -1.391037103 -0.240299852 -0.009912644
## [998,] -1.56202574 0.73818667 -1.391037103 -0.240299852 -0.009912644
## [999,] 0.33333178 1.74343797 0.354332583 -0.807704688 -0.796054418
## [1000,] -1.10254513 0.90572856 -1.391037103 -0.630390677 -0.689819043
## [1001,] -0.47075929 -0.54630110 0.148994973 -0.240299852 -0.541089518
## [1002,] 0.90768254 -0.99307945 0.559670193 -0.736779083 -0.626077818
## [1003,] 0.44820193 -1.32816322 0.302998181 -0.346688258 -0.519842443
## [1004,] -0.87280483 -0.93723216 0.251663778 -0.524002270 -0.434854143
## [1005,] -0.06871376 -0.54630110 0.097660571 -0.665853479 -0.137395094
## [1006,] -0.87280483 -0.93723216 0.251663778 -0.524002270 -0.434854143
## [1007,] 0.44820193 -1.32816322 0.302998181 -0.346688258 -0.519842443
## [1008,] 0.44820193 -1.27231592 0.354332583 -0.382151061 -0.498595368
## [1009,] 0.33333178 -0.99307945 0.662338998 0.752658612 0.478770080
## [1010,] 0.73537731 -0.15537004 0.457001388 0.185253776 0.606252530
## [1011,] 0.33333178 -1.38401051 0.919011011 -0.594927874 -0.434854143
## [1012,] 0.33333178 -1.16062133 0.200329376 -0.382151061 0.011334431
## [1013,] -0.35588914 2.66491833 -0.621021065 -0.311225456 0.308793480
## [1014,] -0.47075929 1.01742314 -1.391037103 -0.665853479 0.096322731
## [1015,] -0.18358391 0.29140832 -0.569686662 -0.382151061 0.691240830
## [1016,] 1.25229300 -0.76969027 0.970345414 -0.240299852 -0.264877544
## [1017,] 0.33333178 -0.82553757 0.662338998 -0.240299852 -0.413607068
## [1018,] -0.18358391 -1.94248346 0.508335791 -1.162332711 -0.817301493
## [1019,] -0.18358391 -1.94248346 0.508335791 -1.162332711 -0.817301493
## [1020,] -0.75793468 -0.15537004 -0.672355467 -0.524002270 -0.201136319
## [1021,] 1.71177361 -0.93723216 1.997033464 -0.098448642 0.754982055
## [1022,] 1.71177361 -0.93723216 1.997033464 -0.098448642 0.754982055
## [1023,] -0.75793468 -0.09952274 -0.929027480 -0.311225456 -0.541089518
## [1024,] -0.06871376 -1.16062133 0.765007803 -0.169374247 0.223805180
## [1025,] -0.35588914 0.29140832 -1.339702700 -0.524002270 0.011334431
## [1026,] 0.16102655 1.68759068 -1.391037103 0.185253776 0.160063956
## [1027,] -0.24101899 -1.21646863 0.251663778 -0.453076665 -0.456101218
## [1028,] -1.10254513 1.49212515 -1.391037103 -0.240299852 -0.477348293
## [1029,] -0.64306452 -1.04892675 -0.313014650 -0.027523038 -0.264877544
## [1030,] -0.35588914 0.29140832 -1.339702700 -0.524002270 0.011334431
## [1031,] -0.70049960 0.34725561 -1.391037103 -0.311225456 0.075075656
## [1032,] -0.58562945 0.12386643 -1.339702700 -0.524002270 0.117569806
## [1033,] -0.12614884 1.63174338 -1.391037103 1.107286635 0.160063956
## [1034,] -0.47075929 0.23556102 -0.980361882 0.043402567 0.032581506
## [1035,] 0.33333178 1.21288867 -0.467017857 -0.027523038 -0.222383394
## [1036,] 1.02255269 -0.88138486 0.354332583 -0.098448642 -0.052406794
## [1037,] -0.41332422 -1.21646863 0.354332583 -0.027523038 -0.116148019
## [1038,] -0.58562945 2.13436903 -0.877693077 -0.524002270 -0.286124619
## [1039,] 0.21846162 -0.65799569 0.713673401 2.596724331 -0.201136319
## [1040,] 0.33333178 -0.15537004 -0.313014650 -0.240299852 0.011334431
## [1041,] -0.52819437 2.44152915 -1.391037103 -0.240299852 0.011334431
## [1042,] -0.81536975 -0.21121733 -0.415683455 -0.594927874 -0.179889244
## [1043,] 0.33333178 -0.15537004 -0.313014650 -0.240299852 0.011334431
## [1044,] 0.67794223 -0.76969027 0.713673401 4.511715654 -0.392359993
## [1045,] -1.10254513 -0.76969027 0.302998181 0.539881799 -0.881042718
## [1046,] -0.81536975 -0.49045380 -1.391037103 -0.807704688 -0.371112918
## [1047,] -0.41332422 1.40835420 -1.391037103 -0.594927874 -0.243630469
## [1048,] -0.70049960 -0.54630110 -0.518352260 -0.524002270 -0.116148019
## [1049,] 0.56307208 -0.21121733 0.457001388 -0.594927874 -0.137395094
## [1050,] 0.56307208 -0.15537004 0.457001388 -0.524002270 -0.073653869
## [1051,] -0.70049960 -0.54630110 -0.518352260 -0.524002270 -0.116148019
## [1052,] 0.10359147 -0.37875921 1.637692646 -0.807704688 6.937880869
## [1053,] -1.56202574 0.43102655 -1.134365090 -0.098448642 -0.307371694
## [1054,] -0.01127868 -1.10477404 0.765007803 -0.169374247 -0.371112918
## [1055,] -0.06871376 0.62649208 -0.005008234 -0.382151061 0.160063956
## [1056,] -0.06871376 0.62649208 -0.005008234 -0.382151061 0.160063956
## [1057,] 0.33333178 -0.26706463 1.329686231 1.036361030 0.287546405
## [1058,] -0.41332422 -0.60214839 -0.107677039 0.965435426 0.351287630
## [1059,] 0.90768254 0.01217185 1.535023841 -0.098448642 0.117569806
## [1060,] 0.33333178 -0.26706463 1.329686231 1.036361030 0.287546405
## [1061,] 1.88407884 -1.66324698 1.535023841 -0.524002270 -0.286124619
## [1062,] 0.44820193 -0.71384298 1.175683024 -0.524002270 -0.349865844
## [1063,] -0.18358391 -0.82553757 0.867676609 -0.453076665 0.223805180
## [1064,] 1.07998777 -1.32816322 1.945699062 -0.098448642 -0.264877544
## [1065,] -0.06871376 1.18496503 -0.929027480 -0.382151061 -0.434854143
## [1066,] -0.35588914 0.45895020 -0.467017857 -0.098448642 -0.094900944
## [1067,] -0.98767498 -0.04367545 -0.980361882 -0.098448642 -0.371112918
## [1068,] 1.59690346 -1.21646863 1.329686231 -0.240299852 -0.583583668
## [1069,] 1.59690346 -1.21646863 1.329686231 -0.240299852 -0.583583668
## [1070,] -0.18358391 0.51479750 0.405666986 0.185253776 -0.031159719
## [1071,] 0.56307208 -1.10477404 0.919011011 -0.736779083 -0.647324893
## [1072,] -0.47075929 1.35250691 -0.364349052 3.944310817 0.223805180
## [1073,] -0.64306452 -0.99307945 -0.056342637 -0.524002270 -0.094900944
## [1074,] -0.18358391 0.51479750 0.302998181 0.114328171 0.011334431
## [1075,] -0.47075929 1.35250691 -0.364349052 3.944310817 0.223805180
## [1076,] 0.44820193 -1.55155239 0.354332583 -0.382151061 -0.349865844
## [1077,] 0.90768254 -1.16062133 1.483689439 -0.382151061 -0.307371694
## [1078,] 0.16102655 -0.88138486 1.945699062 2.738575540 -0.158642169
## [1079,] 0.16102655 -0.88138486 1.945699062 2.738575540 -0.158642169
## [1080,] -0.24101899 -1.27231592 2.099702269 4.086162026 -0.796054418
## [1081,] 1.13742284 -1.43985781 1.483689439 -0.807704688 -0.859795643
## [1082,] -0.24101899 -1.27231592 2.099702269 4.086162026 -0.796054418
## [1083,] -0.64306452 -0.82553757 0.148994973 -0.524002270 -0.307371694
## [1084,] 0.21846162 -0.60214839 0.919011011 -0.098448642 -0.328618769
## [1085,] -0.64306452 -0.82553757 0.148994973 -0.524002270 -0.307371694
## [1086,] -0.87280483 -0.26706463 -0.980361882 -0.524002270 -0.286124619
## [1087,] 0.10359147 -1.04892675 0.662338998 1.532840262 -0.689819043
## [1088,] -0.24101899 -1.88663616 0.765007803 -0.665853479 -0.647324893
## [1089,] 1.88407884 -0.65799569 1.381020634 -0.736779083 0.160063956
## [1090,] 1.88407884 -0.65799569 1.381020634 -0.736779083 0.160063956
## [1091,] 0.96511762 -1.49570510 1.381020634 -0.453076665 -0.094900944
## [1092,] -0.24101899 -1.04892675 0.765007803 -0.382151061 -0.031159719
## [1093,] -0.75793468 0.06801914 -0.929027480 -0.382151061 -0.137395094
## [1094,] 0.50563700 -1.21646863 0.457001388 -0.240299852 -0.179889244
## [1095,] -0.98767498 1.10119409 -0.929027480 2.100245099 0.627499605
## [1096,] 0.62050716 -0.71384298 1.021679816 -0.027523038 -0.009912644
## [1097,] -0.98767498 1.10119409 -0.929027480 2.100245099 0.627499605
## [1098,] 0.16102655 -0.04367545 0.559670193 -0.736779083 0.181311031
## [1099,] -0.18358391 -1.21646863 0.919011011 -0.311225456 2.730960026
## [1100,] 0.16102655 -0.04367545 0.559670193 -0.736779083 0.181311031
## [1101,] 0.04615639 -1.04892675 0.765007803 -0.311225456 -0.328618769
## [1102,] -0.52819437 -0.21121733 -0.005008234 -0.311225456 -0.349865844
## [1103,] -1.27485036 -0.26706463 -0.929027480 -0.594927874 -0.201136319
## [1104,] -0.52819437 -0.21121733 -0.005008234 -0.311225456 -0.349865844
## [1105,] -0.18358391 -0.26706463 0.354332583 -0.240299852 -0.307371694
## [1106,] -1.15998021 0.23556102 0.046326168 -0.311225456 -0.838548568
## [1107,] -0.06871376 -1.66324698 0.765007803 -0.453076665 -0.392359993
## [1108,] 0.44820193 -1.27231592 0.713673401 -0.382151061 -0.413607068
## [1109,] -0.12614884 1.40835420 -0.877693077 0.539881799 0.053828581
## [1110,] 1.42459823 -0.32291192 0.816342206 -0.311225456 1.774841653
## [1111,] -0.01127868 0.01217185 -1.391037103 -0.807704688 -0.371112918
## [1112,] -1.67689590 -0.60214839 -0.005008234 -0.382151061 0.096322731
## [1113,] -0.24101899 -1.10477404 0.713673401 -0.736779083 -0.668571968
## [1114,] 0.33333178 -1.60739969 0.611004596 -0.665853479 -0.286124619
## [1115,] -1.90663620 -0.71384298 1.175683024 1.249137844 -0.881042718
## [1116,] -0.75793468 0.90572856 -1.031696285 -0.027523038 0.075075656
## [1117,] -0.75793468 0.90572856 -1.031696285 -0.027523038 0.075075656
## [1118,] -0.75793468 0.90572856 -1.031696285 -0.027523038 0.075075656
## [1119,] -0.70049960 -0.76969027 -0.775024272 -0.311225456 -0.477348293
## [1120,] -1.56202574 0.73818667 -1.391037103 -0.027523038 -0.456101218
## [1121,] -0.24101899 0.06801914 0.354332583 -0.027523038 -0.243630469
## [1122,] -0.98767498 -0.15537004 -1.391037103 -0.524002270 -0.541089518
## [1123,] -1.15998021 -0.32291192 -1.391037103 -0.807704688 -0.689819043
## [1124,] 1.36716315 -0.71384298 0.508335791 -0.453076665 -0.137395094
## [1125,] -1.04511006 0.29140832 -1.391037103 -0.240299852 0.181311031
## [1126,] 0.27589670 -1.60739969 0.405666986 -0.594927874 -0.689819043
## [1127,] -1.44715559 -1.32816322 -0.056342637 -0.594927874 -0.519842443
## [1128,] -1.15998021 1.29665962 -1.391037103 0.256179380 -0.328618769
## [1129,] 0.96511762 -0.54630110 0.302998181 0.114328171 0.160063956
## [1130,] 1.25229300 -0.54630110 0.405666986 0.539881799 0.096322731
## [1131,] 0.44820193 0.40310291 -1.391037103 -0.453076665 -0.626077818
## [1132,] -1.38972052 -1.88663616 -0.313014650 -0.594927874 -0.902289793
## [1133,] -0.52819437 -0.93723216 0.354332583 -0.524002270 -0.264877544
## [1134,] -0.64306452 -0.26706463 -1.031696285 2.100245099 0.032581506
## [1135,] 0.10359147 -1.38401051 0.405666986 -0.594927874 -0.562336593
## [1136,] -0.18358391 -1.55155239 0.816342206 -0.594927874 -0.434854143
## [1137,] 1.19485792 -0.04367545 0.919011011 -0.382151061 -0.158642169
## [1138,] 1.19485792 -0.04367545 0.919011011 -0.382151061 -0.158642169
## [1139,] -0.47075929 -0.65799569 -0.621021065 0.823584217 0.351287630
## [1140,] -0.06871376 -0.09952274 -0.159011442 -0.382151061 -0.179889244
## [1141,] -0.58562945 -0.71384298 0.148994973 -0.594927874 -0.158642169
## [1142,] -0.06871376 -0.82553757 0.251663778 -0.027523038 -0.158642169
## [1143,] -0.81536975 -0.43460651 -0.826358675 -0.098448642 -0.944783943
## [1144,] -0.75793468 -1.71909428 0.148994973 -0.524002270 -0.477348293
## [1145,] -0.58562945 -1.16062133 -0.210345844 -0.169374247 -0.456101218
## [1146,] -0.06871376 -1.83078887 0.816342206 -0.027523038 -0.243630469
## [1147,] -0.29845406 -0.15537004 -0.775024272 -0.524002270 1.923571178
## [1148,] 0.96511762 -0.65799569 0.919011011 2.596724331 -0.349865844
## [1149,] -0.29845406 -0.76969027 0.765007803 -0.382151061 -0.031159719
## [1150,] 0.96511762 -0.99307945 1.021679816 -0.382151061 -0.562336593
## [1151,] -0.06871376 -1.10477404 0.251663778 0.185253776 -0.434854143
## [1152,] -1.27485036 0.29140832 -0.210345844 -0.027523038 -0.923536868
## [1153,] -0.01127868 0.40310291 -0.107677039 -0.240299852 0.648746680
## [1154,] 0.73537731 -0.60214839 0.405666986 -0.311225456 -0.094900944
## [1155,] -0.98767498 0.29140832 -1.391037103 -0.240299852 0.266299330
## [1156,] -0.01127868 0.40310291 -0.107677039 -0.240299852 0.648746680
## [1157,] 0.10359147 -1.94248346 1.227017426 -0.559465072 -0.349865844
## [1158,] -1.84920113 -0.09952274 -0.467017857 -0.311225456 -0.966031017
## [1159,] -0.93023990 -0.65799569 0.816342206 0.185253776 -0.243630469
## [1160,] 1.07998777 -0.65799569 0.816342206 -0.240299852 0.478770080
## [1161,] 1.30972807 -0.93723216 1.535023841 -0.169374247 -0.009912644
## [1162,] 0.27589670 -0.43460651 0.816342206 -0.807704688 -0.243630469
## [1163,] 0.10359147 -1.16062133 0.765007803 -0.169374247 -0.264877544
## [1164,] 0.39076685 1.43627785 -0.159011442 -0.594927874 -0.201136319
## [1165,] 0.39076685 1.43627785 -0.159011442 -0.594927874 -0.201136319
## [1166,] 0.10359147 -0.49045380 1.175683024 -0.453076665 5.981762496
## [1167,] 0.90768254 0.06801914 -0.056342637 -0.382151061 0.500017155
## [1168,] -0.06871376 -1.10477404 0.611004596 -0.027523038 -0.286124619
## [1169,] -1.04511006 -1.04892675 -0.005008234 0.185253776 -0.434854143
## [1170,] -0.41332422 -0.15537004 0.097660571 -0.169374247 -0.031159719
## [1171,] 0.50563700 -0.93723216 0.354332583 -0.665853479 -0.541089518
## [1172,] -0.70049960 0.34725561 -1.391037103 -0.240299852 -0.201136319
## [1173,] 0.79281239 -0.60214839 0.970345414 -0.311225456 -0.286124619
## [1174,] -0.41332422 -0.93723216 0.200329376 -0.594927874 -0.179889244
## [1175,] -0.41332422 -0.93723216 0.200329376 -0.594927874 -0.179889244
## [1176,] -1.04511006 0.45895020 -1.391037103 -0.240299852 0.160063956
## [1177,] -1.04511006 1.96682715 -1.237033895 2.171170703 -0.179889244
## [1178,] -0.70049960 0.73818667 -1.391037103 -0.098448642 -0.753560268
## [1179,] -1.56202574 2.16229268 -1.391037103 -0.311225456 -0.987278092
## [1180,] -0.06871376 -0.99307945 0.302998181 -0.098448642 -0.243630469
## [1181,] -0.06871376 -0.99307945 0.302998181 -0.098448642 -0.243630469
## [1182,] 0.85024746 -0.76969027 0.816342206 -0.630390677 -0.413607068
## [1183,] 1.07998777 -0.71384298 0.662338998 -0.027523038 -0.413607068
## [1184,] -0.87280483 0.73818667 -1.031696285 -0.665853479 -0.371112918
## [1185,] -0.93023990 0.62649208 -0.210345844 -0.311225456 -0.158642169
## [1186,] -0.75793468 -0.54630110 0.148994973 -0.382151061 -0.052406794
## [1187,] -0.98767498 1.52004879 -1.237033895 3.731534004 -0.179889244
## [1188,] -0.75793468 -0.54630110 0.148994973 -0.382151061 -0.052406794
## [1189,] -0.93023990 0.62649208 -0.210345844 -0.311225456 -0.158642169
## [1190,] 0.27589670 2.38568186 -1.134365090 -0.524002270 -0.264877544
## [1191,] 0.44820193 -0.71384298 1.535023841 1.461914658 -0.158642169
## [1192,] -1.04511006 1.99475079 -1.391037103 -0.169374247 1.668606278
## [1193,] -0.64306452 -1.55155239 0.508335791 -0.027523038 -0.519842443
## [1194,] -1.10254513 1.99475079 -1.391037103 -0.169374247 1.668606278
## [1195,] -0.75793468 1.21288867 -0.775024272 -0.524002270 0.563758380
## [1196,] -1.21741529 -0.54630110 -0.261680247 -0.524002270 -0.201136319
## [1197,] -0.24101899 0.29140832 -0.210345844 -0.169374247 -0.243630469
## [1198,] -0.35588914 0.23556102 -0.313014650 -0.736779083 -0.392359993
## [1199,] -0.35588914 -1.49570510 -0.056342637 -0.382151061 -0.753560268
## [1200,] -0.24101899 0.29140832 -0.210345844 -0.169374247 -0.243630469
## [1201,] -0.35588914 0.23556102 -0.313014650 -0.736779083 -0.392359993
## [1202,] -0.24101899 -1.04892675 0.457001388 -0.453076665 -0.477348293
## [1203,] 0.16102655 -0.60214839 0.611004596 -0.524002270 -0.413607068
## [1204,] 0.90768254 1.18496503 -0.415683455 2.313021912 0.500017155
## [1205,] -0.64306452 -0.93723216 0.970345414 -0.311225456 -0.286124619
## [1206,] -0.64306452 -0.93723216 0.970345414 -0.311225456 -0.286124619
## [1207,] -0.64306452 -0.93723216 0.970345414 -0.311225456 -0.286124619
## [1208,] 0.90768254 1.07327044 1.432355036 -0.594927874 1.031194029
## [1209,] -0.64306452 -0.93723216 0.970345414 -0.311225456 -0.286124619
## [1210,] -1.21741529 -0.76969027 0.816342206 -0.382151061 -0.349865844
## [1211,] -0.87280483 0.68233938 -1.288368298 -0.311225456 -0.201136319
## [1212,] -0.98767498 -0.49045380 -0.621021065 -0.311225456 -0.243630469
## [1213,] -0.87280483 0.68233938 -1.288368298 -0.311225456 -0.201136319
## [1214,] 0.73537731 -0.82553757 0.765007803 -0.453076665 -0.349865844
## [1215,] 1.07998777 -1.10477404 0.970345414 -0.453076665 -0.137395094
## [1216,] 0.27589670 -1.43985781 0.970345414 -0.311225456 0.160063956
## [1217,] -0.24101899 0.23556102 0.200329376 -0.382151061 -0.179889244
## [1218,] -0.06871376 -1.04892675 0.508335791 -0.453076665 -0.647324893
## [1219,] -0.06871376 -0.71384298 0.200329376 -0.453076665 -0.116148019
## [1220,] 0.39076685 -0.76969027 0.662338998 -0.878630293 -0.923536868
## [1221,] 1.48203330 -1.16062133 1.278351829 -0.524002270 0.946205729
## [1222,] 1.48203330 -1.16062133 1.278351829 -0.524002270 0.946205729
## [1223,] -0.12614884 0.01217185 -0.261680247 -0.240299852 -0.201136319
## [1224,] 1.25229300 -0.93723216 1.021679816 -0.240299852 -0.286124619
## [1225,] 2.45842960 -0.76969027 1.124348621 -0.027523038 -0.158642169
## [1226,] 0.50563700 -0.37875921 -0.210345844 0.043402567 0.075075656
## [1227,] -0.47075929 0.29140832 -1.237033895 1.107286635 -0.158642169
## [1228,] 0.39076685 0.29140832 -0.107677039 -0.382151061 0.351287630
## [1229,] -1.84920113 -0.60214839 -1.391037103 -0.524002270 -0.923536868
## [1230,] -0.41332422 -0.54630110 0.097660571 -0.311225456 -0.264877544
## [1231,] -0.35588914 -1.94248346 0.354332583 0.114328171 -0.456101218
## [1232,] -0.29845406 1.60381973 -1.339702700 0.043402567 -0.286124619
## [1233,] -0.41332422 -0.54630110 0.097660571 -0.311225456 -0.264877544
## [1234,] 1.07998777 -1.66324698 0.508335791 -0.240299852 -0.647324893
## [1235,] -0.70049960 1.24081232 -1.339702700 -0.240299852 -0.604830743
## [1236,] -1.33228544 -1.10477404 0.251663778 7.348739836 -0.711066118
## [1237,] -0.29845406 0.12386643 -1.391037103 -0.594927874 -0.371112918
## [1238,] -0.70049960 1.24081232 -1.339702700 -0.240299852 -0.604830743
## [1239,] -0.12614884 1.12911773 -1.391037103 -0.027523038 -0.137395094
## [1240,] -1.04511006 0.79403397 -1.391037103 1.249137844 -0.647324893
## [1241,] -0.47075929 0.45895020 -0.364349052 -0.594927874 -0.243630469
## [1242,] 0.85024746 -0.88138486 0.611004596 -0.027523038 -0.179889244
## [1243,] 0.39076685 -0.71384298 0.713673401 -0.382151061 -0.626077818
## [1244,] -0.01127868 0.17971373 -0.261680247 -0.098448642 -0.116148019
## [1245,] -1.38972052 -1.32816322 -0.107677039 7.703367859 -0.434854143
## [1246,] -0.52819437 0.12386643 -0.415683455 -0.524002270 -0.116148019
## [1247,] -0.52819437 1.18496503 -1.031696285 -0.594927874 -0.031159719
## [1248,] -0.52819437 0.12386643 -0.415683455 -0.524002270 -0.116148019
## [1249,] -0.81536975 -0.65799569 0.302998181 -0.240299852 -0.137395094
## [1250,] -0.70049960 0.40310291 -1.339702700 -0.169374247 -0.179889244
## [1251,] -0.70049960 0.40310291 -1.339702700 -0.169374247 -0.179889244
## [1252,] -0.47075929 0.29140832 -0.672355467 -0.240299852 -0.222383394
## [1253,] -0.70049960 1.07327044 -1.391037103 -0.524002270 0.754982055
## [1254,] -0.24101899 0.73818667 -1.391037103 -0.807704688 0.181311031
## [1255,] -0.29845406 0.96157585 -1.083030687 -0.453076665 -0.179889244
## [1256,] -1.27485036 0.62649208 -1.288368298 -0.098448642 -0.392359993
## [1257,] -0.47075929 0.34725561 -0.261680247 -0.524002270 -0.116148019
## [1258,] -0.75793468 0.29140832 0.046326168 1.603765867 -0.052406794
## [1259,] -0.87280483 0.62649208 -1.391037103 0.114328171 0.754982055
## [1260,] -0.87280483 0.62649208 -1.391037103 0.114328171 0.754982055
## [1261,] 0.16102655 0.59856844 2.099702269 -0.524002270 6.704163044
## [1262,] -1.15998021 2.74868927 -1.391037103 -0.382151061 -0.094900944
## [1263,] 0.85024746 -0.43460651 0.559670193 -0.027523038 -0.137395094
## [1264,] -0.06871376 1.40835420 -1.391037103 -0.240299852 0.032581506
## [1265,] 0.10359147 -0.88138486 0.251663778 -0.524002270 -0.456101218
## [1266,] -0.64306452 0.23556102 -1.134365090 -0.169374247 -0.137395094
## [1267,] -0.64306452 0.23556102 -1.134365090 -0.169374247 -0.137395094
## [1268,] 1.19485792 -0.54630110 1.175683024 -0.169374247 -0.413607068
## [1269,] -0.81536975 -0.65799569 0.200329376 -0.382151061 -0.179889244
## [1270,] -1.61946082 -0.21121733 -1.237033895 -0.524002270 -0.923536868
## [1271,] -1.90663620 -0.82553757 -1.339702700 -0.665853479 -0.838548568
## [1272,] -0.58562945 -0.49045380 -0.364349052 -0.665853479 -0.817301493
## [1273,] -1.38972052 -0.37875921 -1.391037103 -0.453076665 -0.222383394
## [1274,] -0.47075929 0.29140832 -0.364349052 -0.382151061 -0.307371694
## [1275,] -0.29845406 0.29140832 -0.723689870 -0.311225456 0.308793480
## [1276,] -0.18358391 1.04534679 -0.261680247 -0.169374247 -0.264877544
## [1277,] 0.10359147 -0.71384298 0.662338998 2.667649935 -0.796054418
## [1278,] -0.75793468 0.90572856 -1.391037103 -0.453076665 0.563758380
## [1279,] -0.18358391 1.04534679 -0.261680247 -0.169374247 -0.264877544
## [1280,] 0.85024746 -1.27231592 0.611004596 -0.594927874 -0.541089518
## [1281,] -0.70049960 -0.37875921 -0.364349052 -0.453076665 -0.222383394
## [1282,] -0.70049960 -0.37875921 -0.364349052 -0.453076665 -0.222383394
## [1283,] -0.24101899 1.32458326 -1.391037103 -0.382151061 -0.073653869
## [1284,] 0.21846162 0.57064479 0.046326168 0.114328171 0.181311031
## [1285,] -0.75793468 -0.60214839 -0.415683455 -0.169374247 -0.349865844
## [1286,] 1.71177361 -0.88138486 1.175683024 -0.524002270 0.053828581
## [1287,] -0.70049960 -2.05417804 0.867676609 -0.027523038 -0.413607068
## [1288,] -0.18358391 0.40310291 -0.980361882 0.043402567 -0.668571968
## [1289,] -0.75793468 0.40310291 0.148994973 1.390989053 -0.413607068
## [1290,] -0.75793468 0.40310291 0.148994973 1.390989053 -0.413607068
## [1291,] -0.41332422 1.18496503 -1.391037103 -0.453076665 0.266299330
## [1292,] -0.06871376 0.59856844 -0.877693077 -0.311225456 -0.307371694
## [1293,] -1.38972052 -0.74176663 -0.723689870 -0.098448642 -0.668571968
## [1294,] -0.47075929 1.26873597 -1.391037103 -0.453076665 -0.073653869
## [1295,] -0.06871376 0.59856844 -0.877693077 -0.311225456 -0.307371694
## [1296,] -0.98767498 0.57064479 -1.391037103 1.249137844 0.117569806
## [1297,] -0.98767498 0.57064479 -1.391037103 1.249137844 0.117569806
## [1298,] -0.64306452 0.01217185 -0.672355467 -0.311225456 -0.498595368
## [1299,] -1.50459067 0.40310291 -1.391037103 -0.807704688 -0.519842443
## [1300,] -0.41332422 5.87613775 -1.391037103 -0.311225456 1.052441104
## [1301,] -1.79176605 0.65441573 -1.391037103 -0.275762654 -0.158642169
## [1302,] -0.93023990 1.85513256 -1.031696285 -0.382151061 0.266299330
## [1303,] 0.44820193 -0.88138486 0.251663778 -0.311225456 -0.498595368
## [1304,] -0.18358391 -1.38401051 0.867676609 -0.524002270 -0.137395094
## [1305,] -0.41332422 1.46420150 -0.313014650 -0.169374247 -0.009912644
## [1306,] -0.47075929 0.45895020 -0.056342637 -0.453076665 -0.307371694
## [1307,] 0.79281239 0.90572856 0.251663778 -0.027523038 0.011334431
## [1308,] -0.87280483 0.84988126 -0.929027480 0.965435426 -0.413607068
## [1309,] 0.79281239 0.90572856 0.251663778 -0.027523038 0.011334431
## [1310,] -0.75793468 0.51479750 -0.877693077 -0.807704688 -0.349865844
## [1311,] -0.47075929 0.45895020 -0.056342637 -0.453076665 -0.307371694
## [1312,] -1.04511006 -0.09952274 -0.621021065 0.327104985 -0.498595368
## [1313,] -0.18358391 3.64224598 -0.313014650 -0.453076665 -0.094900944
## [1314,] -0.75793468 -0.93723216 -0.313014650 -0.169374247 -0.031159719
## [1315,] -0.75793468 -0.93723216 -0.313014650 -0.098448642 -0.031159719
## [1316,] -0.47075929 0.57064479 -0.005008234 -0.382151061 -0.094900944
## [1317,] -1.67689590 1.18496503 -1.391037103 -0.949555897 -0.987278092
## [1318,] 0.90768254 -0.49045380 0.970345414 -0.240299852 0.075075656
## [1319,] -0.47075929 0.57064479 -0.005008234 -0.382151061 -0.094900944
## [1320,] 0.44820193 1.29665962 2.099702269 -0.594927874 6.937880869
## [1321,] 0.79281239 0.73818667 0.354332583 0.043402567 0.138816881
## [1322,] -1.90663620 1.18496503 -1.391037103 -0.949555897 -0.987278092
## [1323,] 0.44820193 -1.04892675 0.765007803 -0.524002270 -0.626077818
## [1324,] 0.44820193 -0.93723216 0.611004596 -0.524002270 -0.583583668
## [1325,] -0.93023990 -0.37875921 -0.159011442 -0.594927874 -0.222383394
## [1326,] -0.93023990 -0.37875921 -0.159011442 -0.594927874 -0.222383394
## [1327,] -0.93023990 -0.37875921 -0.159011442 -0.594927874 -0.222383394
## [1328,] -0.93023990 -0.37875921 -0.159011442 -0.594927874 -0.222383394
## [1329,] -1.04511006 -0.04367545 -0.826358675 -0.524002270 -0.307371694
## [1330,] -0.52819437 0.40310291 -0.056342637 -0.311225456 -0.094900944
## [1331,] -0.52819437 0.40310291 -0.056342637 -0.311225456 -0.094900944
## [1332,] -0.29845406 1.91097985 -0.056342637 0.894509821 0.415028855
## [1333,] 0.04615639 -0.76969027 -0.877693077 -0.594927874 -0.264877544
## [1334,] 0.44820193 1.38043056 -0.261680247 -0.240299852 -0.179889244
## [1335,] -0.64306452 1.71551432 -1.391037103 -0.382151061 1.668606278
## [1336,] -0.98767498 0.29140832 -1.288368298 -0.098448642 -0.392359993
## [1337,] -1.33228544 -0.15537004 -1.391037103 -0.807704688 -0.647324893
## [1338,] -1.33228544 -0.15537004 -1.391037103 -0.807704688 -0.647324893
## [1339,] -1.33228544 -0.15537004 -1.391037103 -0.807704688 -0.647324893
## [1340,] -0.47075929 -0.09952274 -1.288368298 -0.594927874 -0.073653869
## [1341,] -0.47075929 -0.09952274 -1.288368298 -0.594927874 -0.073653869
## [1342,] -0.47075929 -0.09952274 -1.288368298 -0.594927874 -0.073653869
## [1343,] -0.41332422 0.06801914 -1.288368298 -0.594927874 -0.052406794
## [1344,] -0.47075929 -0.09952274 -1.288368298 -0.594927874 -0.073653869
## [1345,] 1.82664376 -0.60214839 1.073014219 0.043402567 -0.222383394
## [1346,] -0.06871376 -0.49045380 -0.159011442 -0.169374247 -0.519842443
## [1347,] -1.27485036 0.34725561 -1.339702700 -0.311225456 -0.668571968
## [1348,] -0.64306452 0.71026303 -1.237033895 -0.524002270 -0.201136319
## [1349,] -0.64306452 0.71026303 -1.237033895 -0.524002270 -0.201136319
## [1350,] -0.81536975 0.23556102 -1.391037103 0.185253776 -0.137395094
## [1351,] 0.39076685 0.40310291 0.097660571 -0.382151061 -0.392359993
## [1352,] -0.64306452 0.51479750 -1.339702700 -0.169374247 -0.477348293
## [1353,] -0.41332422 0.65441573 -1.237033895 -0.453076665 -0.031159719
## [1354,] -0.41332422 0.65441573 -1.237033895 -0.453076665 -0.031159719
## [1355,] -0.64306452 0.29140832 -1.237033895 -0.169374247 -0.222383394
## [1356,] -1.27485036 -1.16062133 -0.107677039 -0.524002270 -0.031159719
## [1357,] -1.27485036 -1.04892675 -0.107677039 -0.524002270 -0.073653869
## [1358,] -0.58562945 -0.54630110 -0.159011442 -0.027523038 -0.201136319
## [1359,] -0.52819437 0.62649208 -0.518352260 2.029319494 1.711100428
## [1360,] 1.88407884 -0.29498827 0.662338998 -0.807704688 0.075075656
## [1361,] 0.50563700 0.06801914 0.200329376 -0.169374247 0.521264230
## [1362,] -0.01127868 1.79928526 -0.672355467 -0.027523038 0.117569806
## [1363,] 1.88407884 -0.29498827 0.662338998 -0.807704688 0.075075656
## [1364,] -0.18358391 1.68759068 -0.005008234 -0.382151061 -0.158642169
## [1365,] -0.64306452 0.43102655 -1.288368298 -0.453076665 0.181311031
## [1366,] -0.29845406 -0.15537004 -0.929027480 -0.240299852 0.585005455
## [1367,] -0.58562945 1.18496503 -0.980361882 -0.594927874 0.138816881
## [1368,] -0.81536975 0.06801914 0.148994973 -0.240299852 0.011334431
## [1369,] -0.18358391 1.35250691 0.251663778 -0.311225456 -0.179889244
## [1370,] -0.98767498 0.45895020 -1.391037103 -0.665853479 -0.392359993
## [1371,] 0.21846162 1.40835420 1.227017426 -0.594927874 6.959127944
## [1372,] -0.47075929 0.29140832 1.483689439 0.398030589 1.392394304
## [1373,] 0.21846162 1.40835420 1.227017426 -0.594927874 6.959127944
## [1374,] -0.35588914 1.24081232 -0.005008234 0.894509821 0.478770080
## [1375,] -0.87280483 1.60381973 -1.391037103 -0.949555897 3.814560849
## [1376,] -0.64306452 0.17971373 -0.056342637 -0.382151061 -0.094900944
## [1377,] -0.06871376 1.99475079 -0.364349052 -0.807704688 -0.031159719
## [1378,] -1.79176605 -0.21121733 -0.056342637 -0.169374247 0.053828581
## [1379,] -0.64306452 -0.43460651 -0.621021065 -0.382151061 -0.201136319
## [1380,] -0.47075929 0.23556102 -1.288368298 0.043402567 -0.222383394
## [1381,] -0.47075929 0.23556102 -1.288368298 0.043402567 -0.222383394
## [1382,] -0.87280483 1.68759068 -0.929027480 -0.524002270 -0.286124619
## [1383,] -0.18358391 0.40310291 -0.261680247 -0.311225456 -0.158642169
## [1384,] -0.18358391 0.40310291 -0.261680247 -0.311225456 -0.158642169
## [1385,] -0.70049960 1.26873597 -0.621021065 -0.524002270 0.415028855
## [1386,] -0.18358391 1.57589609 -0.107677039 0.610807403 -0.243630469
## [1387,] -0.52819437 0.62649208 -1.031696285 -0.524002270 0.266299330
## [1388,] -0.52819437 0.62649208 -1.031696285 -0.524002270 0.266299330
## [1389,] -0.98767498 0.62649208 0.200329376 2.525798726 -0.094900944
## [1390,] -0.93023990 -0.26706463 -1.288368298 -0.240299852 -0.158642169
## [1391,] -1.33228544 -0.21121733 -1.391037103 -0.169374247 -0.413607068
## [1392,] -0.18358391 0.62649208 -0.261680247 -0.098448642 0.138816881
## [1393,] -0.70049960 0.51479750 -1.083030687 -0.878630293 -0.371112918
## [1394,] -0.18358391 -0.04367545 -0.107677039 -0.382151061 -0.201136319
## [1395,] -1.10254513 0.23556102 -0.672355467 0.965435426 -0.371112918
## [1396,] 0.16102655 0.87780491 -0.877693077 -0.665853479 0.096322731
## [1397,] 0.21846162 0.82195761 -0.877693077 -0.665853479 0.053828581
## [1398,] -0.58562945 0.34725561 -0.056342637 -0.382151061 -0.158642169
## [1399,] -0.75793468 0.40310291 -0.775024272 -0.240299852 -0.094900944
## [1400,] -0.64306452 0.79403397 -1.391037103 -0.240299852 -0.413607068
## [1401,] -0.24101899 0.90572856 -0.313014650 -0.311225456 -0.158642169
## [1402,] -0.24101899 0.90572856 -0.313014650 -0.311225456 -0.158642169
## [1403,] -0.41332422 -1.27231592 0.765007803 -0.382151061 -0.753560268
## [1404,] -0.64306452 -1.10477404 0.302998181 -0.594927874 -0.562336593
## [1405,] -0.18358391 -0.15537004 0.611004596 0.043402567 -0.116148019
## [1406,] -0.35588914 -1.38401051 0.148994973 -0.382151061 -0.541089518
## [1407,] -0.06871376 -1.60739969 0.354332583 1.816542681 -0.541089518
## [1408,] -1.33228544 -0.09952274 -1.391037103 -0.311225456 -0.498595368
## [1409,] -0.12614884 -1.32816322 0.457001388 -0.240299852 -0.838548568
## [1410,] -1.33228544 -0.09952274 -1.391037103 -0.311225456 -0.498595368
## [1411,] -0.98767498 2.41360550 -1.391037103 -0.524002270 -0.116148019
## [1412,] -1.10254513 -0.32291192 0.662338998 -0.098448642 -0.349865844
## [1413,] -0.06871376 -1.60739969 0.354332583 1.816542681 -0.541089518
## [1414,] 0.90768254 0.23556102 -0.107677039 -0.382151061 0.351287630
## [1415,] 0.96511762 -1.16062133 1.637692646 -0.240299852 -0.222383394
## [1416,] -1.21741529 0.29140832 -1.391037103 -0.665853479 -0.477348293
## [1417,] 0.96511762 -1.16062133 1.637692646 -0.240299852 -0.222383394
## [1418,] -0.58562945 -1.04892675 0.302998181 -0.027523038 -0.498595368
## [1419,] -0.29845406 0.01217185 -1.339702700 -0.665853479 -0.222383394
## [1420,] -0.35588914 0.62649208 -0.313014650 -0.240299852 -0.222383394
## [1421,] -0.29845406 0.01217185 -1.339702700 -0.665853479 -0.222383394
## [1422,] -0.47075929 -0.71384298 -0.467017857 -0.665853479 -0.179889244
## [1423,] -0.75793468 0.06801914 -1.391037103 -0.311225456 -0.179889244
## [1424,] -1.10254513 0.01217185 -0.929027480 0.965435426 0.754982055
## [1425,] -0.01127868 -1.49570510 0.508335791 -0.807704688 -0.243630469
## [1426,] -0.01127868 -1.49570510 0.508335791 -0.807704688 -0.243630469
## [1427,] -0.35588914 -1.66324698 0.508335791 -0.524002270 -0.881042718
## [1428,] -0.41332422 -0.65799569 0.302998181 -0.027523038 -0.201136319
## [1429,] -0.29845406 0.62649208 -1.391037103 -0.453076665 -0.328618769
## [1430,] -0.24101899 -1.94248346 0.662338998 -0.240299852 -0.817301493
## [1431,] -0.52819437 -0.65799569 -0.159011442 -0.524002270 -0.456101218
## [1432,] -0.41332422 -0.54630110 0.200329376 -0.311225456 -0.392359993
## [1433,] -1.38972052 -0.49045380 -1.391037103 -0.665853479 -0.966031017
## [1434,] -1.27485036 -0.71384298 -0.569686662 -0.524002270 -0.392359993
## [1435,] 1.07998777 0.06801914 0.508335791 9.121879950 2.688465876
## [1436,] 1.07998777 0.06801914 0.508335791 9.121879950 2.688465876
## [1437,] 0.96511762 -0.82553757 0.559670193 -0.665853479 1.732347503
## [1438,] -0.87280483 2.16229268 0.097660571 1.603765867 -0.371112918
## [1439,] -0.75793468 0.34725561 -1.391037103 -0.594927874 -0.753560268
## [1440,] -0.58562945 0.79403397 -1.288368298 -0.240299852 -0.328618769
## [1441,] -0.64306452 -0.88138486 0.251663778 -0.382151061 -0.541089518
## [1442,] -0.52819437 1.43627785 -0.415683455 1.887468285 0.138816881
## [1443,] -0.81536975 0.57064479 -1.288368298 -0.453076665 -0.201136319
## [1444,] -0.81536975 0.29140832 -0.364349052 -0.559465072 -0.626077818
## [1445,] -0.58562945 0.79403397 -1.288368298 -0.240299852 -0.328618769
## [1446,] -0.52819437 1.43627785 -0.415683455 1.887468285 0.138816881
## [1447,] -0.81536975 0.57064479 -1.288368298 -0.453076665 -0.201136319
## [1448,] -0.87280483 0.79403397 -1.391037103 -0.453076665 -0.158642169
## [1449,] -0.81536975 0.29140832 -1.339702700 -0.453076665 -0.158642169
## [1450,] -0.64306452 -0.82553757 0.200329376 -0.382151061 -0.668571968
## [1451,] -0.64306452 -0.88138486 0.251663778 -0.382151061 -0.541089518
## [1452,] -0.29845406 -1.16062133 0.867676609 0.114328171 0.351287630
## [1453,] -0.98767498 0.29140832 -1.288368298 -0.382151061 -0.541089518
## [1454,] -0.41332422 -0.21121733 0.302998181 -0.453076665 -0.286124619
## [1455,] 1.94151391 -0.43460651 1.843030257 -0.240299852 -0.307371694
## [1456,] -1.04511006 2.07852174 -1.391037103 -0.665853479 -0.753560268
## [1457,] -1.33228544 0.06801914 -1.083030687 -0.524002270 -0.796054418
## [1458,] -0.41332422 -0.21121733 0.302998181 -0.453076665 -0.286124619
## [1459,] 0.04615639 -1.32816322 0.662338998 -0.594927874 -0.434854143
## [1460,] -0.24101899 -1.83078887 0.405666986 -0.594927874 -0.711066118
## [1461,] -1.10254513 -0.60214839 -0.929027480 -0.169374247 -0.711066118
## [1462,] -1.21741529 1.43627785 -1.391037103 -0.311225456 -0.583583668
## [1463,] -0.87280483 0.62649208 -1.237033895 -0.169374247 -0.264877544
## [1464,] -0.81536975 0.57064479 -1.339702700 -0.098448642 -0.243630469
## [1465,] -0.87280483 0.34725561 -0.877693077 -0.594927874 -0.519842443
## [1466,] -0.87280483 0.34725561 -0.877693077 -0.594927874 -0.519842443
## [1467,] -0.58562945 -0.26706463 0.251663778 -0.311225456 -0.541089518
## [1468,] -0.93023990 2.86038386 -0.980361882 -0.169374247 -0.434854143
## [1469,] -0.58562945 -0.26706463 0.251663778 -0.311225456 -0.541089518
## [1470,] -0.58562945 2.52530009 -1.134365090 -0.311225456 -0.562336593
## [1471,] 0.96511762 0.90572856 -0.826358675 -0.807704688 -0.073653869
## [1472,] -0.93023990 0.96157585 -0.980361882 0.859047019 -0.434854143
## [1473,] -0.41332422 -0.99307945 1.689027049 0.043402567 -0.307371694
## [1474,] -1.27485036 0.40310291 -0.980361882 -0.524002270 -0.349865844
## [1475,] 0.90768254 -0.15537004 1.175683024 7.987070277 2.497242202
## [1476,] -1.73433097 -0.32291192 -0.826358675 -0.240299852 -0.838548568
## [1477,] 0.90768254 -0.15537004 1.175683024 7.987070277 2.497242202
## [1478,] -1.73433097 -0.32291192 -0.826358675 -0.240299852 -0.838548568
## [1479,] -0.70049960 1.93890350 -1.134365090 2.242096308 -0.116148019
## [1480,] -0.06871376 -1.38401051 1.689027049 0.327104985 0.351287630
## [1481,] -1.56202574 0.51479750 -1.237033895 -0.736779083 -0.158642169
## [1482,] -0.06871376 -1.38401051 1.689027049 0.327104985 0.351287630
## [1483,] -0.64306452 0.29140832 1.381020634 -0.311225456 0.563758380
## [1484,] -0.12614884 -1.10477404 0.867676609 -0.736779083 -0.966031017
## [1485,] -0.87280483 2.13436903 -1.083030687 -0.382151061 -0.583583668
## [1486,] -0.75793468 0.71026303 -0.569686662 -0.311225456 -0.286124619
## [1487,] -0.87280483 0.84988126 -0.313014650 -0.311225456 -0.371112918
## [1488,] -1.33228544 0.62649208 -1.134365090 -0.453076665 -0.456101218
## [1489,] -1.56202574 0.06801914 -1.185699492 -0.594927874 -0.817301493
## [1490,] -1.21741529 0.23556102 -0.877693077 -0.311225456 -0.838548568
## [1491,] -0.70049960 -1.71909428 1.124348621 -0.524002270 -1.029772242
## [1492,] -1.56202574 0.06801914 -1.185699492 -0.594927874 -0.817301493
## [1493,] -1.21741529 0.68233938 -1.083030687 -0.665853479 -0.796054418
## [1494,] -0.35588914 0.06801914 -0.056342637 -0.453076665 0.032581506
## [1495,] -1.10254513 -1.21646863 -0.929027480 -0.807704688 -0.456101218
## [1496,] -0.75793468 -0.54630110 -1.288368298 -0.453076665 -0.158642169
## [1497,] -0.35588914 0.06801914 -0.056342637 -0.453076665 0.032581506
## [1498,] -0.81536975 1.18496503 -1.237033895 -0.169374247 -0.711066118
## [1499,] -0.98767498 2.05059809 -1.185699492 -0.169374247 -0.413607068
## [1500,] -0.81536975 1.18496503 -1.237033895 -0.169374247 -0.711066118
## [1501,] -0.47075929 1.10119409 -1.185699492 -0.736779083 -0.243630469
## [1502,] -0.29845406 1.63174338 0.097660571 1.249137844 -0.094900944
## [1503,] -0.58562945 0.31933196 -0.467017857 -0.098448642 -0.201136319
## [1504,] -1.21741529 -0.49045380 0.611004596 -0.027523038 -0.222383394
## [1505,] -0.47075929 -0.82553757 1.535023841 -0.169374247 0.393781780
## [1506,] -0.93023990 1.29665962 -1.288368298 -0.524002270 -0.201136319
## [1507,] -0.87280483 1.57589609 -1.134365090 -0.382151061 -0.371112918
## [1508,] -0.47075929 -0.82553757 1.535023841 -0.169374247 0.393781780
## [1509,] -0.70049960 -1.43985781 1.689027049 -0.311225456 -0.286124619
## [1510,] -0.24101899 -1.94248346 0.662338998 -0.524002270 -0.541089518
## [1511,] -1.10254513 -0.93723216 -0.313014650 -0.240299852 -0.859795643
## [1512,] -0.70049960 0.90572856 -1.185699492 -0.311225456 -0.413607068
## [1513,] -1.10254513 1.46420150 -1.185699492 -0.240299852 -0.562336593
## [1514,] -1.10254513 0.17971373 -0.621021065 -0.524002270 -0.201136319
## [1515,] -0.81536975 1.74343797 -0.313014650 1.107286635 -0.286124619
## [1516,] -0.81536975 1.74343797 -0.313014650 1.107286635 -0.286124619
## [1517,] -1.27485036 -1.16062133 -0.107677039 -0.169374247 -0.349865844
## [1518,] -1.04511006 0.01217185 -1.083030687 -0.382151061 -0.519842443
## [1519,] -0.52819437 -0.32291192 0.970345414 -0.240299852 0.563758380
## [1520,] -0.98767498 0.96157585 -0.980361882 0.043402567 0.393781780
## [1521,] -1.04511006 0.01217185 -1.083030687 -0.382151061 -0.519842443
## [1522,] -0.81536975 -0.26706463 -0.364349052 -0.453076665 -0.116148019
## [1523,] -1.27485036 -1.16062133 -0.107677039 -0.169374247 -0.349865844
## [1524,] -0.87280483 -0.26706463 -0.107677039 -0.382151061 -0.243630469
## [1525,] -1.33228544 -0.60214839 -0.415683455 -0.382151061 -0.264877544
## [1526,] -0.93023990 -0.26706463 -0.980361882 -0.311225456 -0.498595368
## [1527,] -0.87280483 -0.32291192 -0.980361882 -0.240299852 -0.498595368
## [1528,] -0.70049960 0.01217185 -1.031696285 -0.594927874 -0.349865844
## [1529,] -0.24101899 -1.32816322 1.124348621 -0.240299852 0.181311031
## [1530,] -0.70049960 0.90572856 -0.980361882 -0.311225456 -0.519842443
## [1531,] -0.98767498 -0.49045380 -0.929027480 -0.240299852 -0.519842443
## [1532,] -1.27485036 0.98949950 -0.877693077 0.185253776 -0.137395094
## [1533,] -0.64306452 0.01217185 -0.723689870 -0.382151061 -0.626077818
## [1534,] -0.18358391 -0.76969027 0.148994973 -0.453076665 -0.286124619
## [1535,] -0.98767498 0.17971373 -0.672355467 -0.098448642 -0.498595368
## [1536,] -0.75793468 0.12386643 -0.723689870 -0.240299852 -0.264877544
## [1537,] -1.27485036 0.01217185 -0.980361882 -0.453076665 -0.222383394
## [1538,] -1.67689590 0.29140832 -0.980361882 -0.453076665 -0.604830743
## [1539,] -1.21741529 0.62649208 -0.929027480 -0.027523038 -0.137395094
## [1540,] -0.64306452 -0.76969027 0.251663778 -0.524002270 -0.477348293
## [1541,] -1.21741529 -0.04367545 -0.980361882 1.320063449 -0.349865844
## [1542,] -0.52819437 -1.55155239 0.097660571 -0.240299852 -0.711066118
## [1543,] -0.93023990 1.82720891 -1.288368298 -0.453076665 -0.498595368
## [1544,] 1.59690346 -0.49045380 0.765007803 -0.240299852 -0.498595368
## [1545,] 0.04615639 -0.88138486 0.816342206 -0.169374247 -0.519842443
## [1546,] -1.04511006 0.57064479 0.302998181 -0.524002270 -0.604830743
## [1547,] -0.75793468 0.23556102 -1.288368298 -0.382151061 -0.328618769
## [1548,] -1.15998021 0.40310291 -0.877693077 -0.665853479 -0.838548568
## [1549,] 1.65433853 -0.71384298 1.175683024 -0.382151061 0.245052255
## [1550,] -0.52819437 -0.93723216 0.148994973 -0.524002270 -0.286124619
## [1551,] -0.70049960 0.84988126 -1.391037103 -0.169374247 -0.009912644
## [1552,] -0.70049960 0.79403397 -1.391037103 -0.169374247 -0.094900944
## [1553,] -1.15998021 0.84988126 -1.339702700 0.823584217 0.330040555
## [1554,] -0.58562945 1.15704138 -1.391037103 -0.240299852 -0.158642169
## [1555,] -0.98767498 1.82720891 -1.288368298 -0.098448642 -0.541089518
## [1556,] -0.75793468 0.17971373 -0.518352260 -0.594927874 -0.477348293
## [1557,] -0.98767498 1.96682715 -1.185699492 -0.240299852 -0.456101218
## [1558,] -0.98767498 1.82720891 -1.288368298 -0.098448642 -0.541089518
## [1559,] -0.81536975 0.57064479 0.302998181 2.951352353 3.134654451
## [1560,] -0.29845406 0.40310291 -0.056342637 -0.382151061 -0.158642169
## [1561,] -0.29845406 0.40310291 -0.056342637 -0.382151061 -0.158642169
## [1562,] -0.29845406 0.40310291 -0.056342637 -0.382151061 -0.158642169
## [1563,] -0.64306452 0.93365220 -0.723689870 -0.382151061 -0.243630469
## [1564,] -0.64306452 0.93365220 -0.723689870 -0.382151061 -0.243630469
## [1565,] -0.64306452 0.93365220 -0.723689870 -0.382151061 -0.243630469
## [1566,] -0.93023990 0.79403397 -1.288368298 -0.453076665 -0.562336593
## [1567,] -0.93023990 -2.05417804 1.894364659 -0.311225456 -0.604830743
## [1568,] -0.64306452 0.93365220 -0.723689870 -0.382151061 -0.243630469
## [1569,] -0.75793468 0.17971373 -0.723689870 -0.665853479 -0.222383394
## [1570,] -1.21741529 -0.09952274 -0.672355467 -0.453076665 -0.668571968
## [1571,] -1.10254513 -0.93723216 1.329686231 -0.240299852 3.028419076
## [1572,] -1.10254513 -0.82553757 -0.672355467 -0.240299852 -1.051019317
## [1573,] -0.58562945 0.90572856 0.251663778 -0.240299852 -0.392359993
## [1574,] -1.33228544 0.29140832 -0.364349052 -0.098448642 -0.264877544
## [1575,] -1.56202574 -1.21646863 2.613046294 8.057995881 -0.286124619
## [1576,] -0.47075929 -0.04367545 0.662338998 -0.240299852 -0.583583668
## [1577,] -0.18358391 -1.27231592 1.843030257 -0.665853479 -0.137395094
## [1578,] -1.21741529 0.96157585 -0.621021065 1.816542681 -0.243630469
## [1579,] -0.87280483 0.79403397 -0.621021065 -0.524002270 0.648746680
## [1580,] -1.21741529 0.17971373 -0.929027480 -0.594927874 -0.732313193
## [1581,] -0.52819437 -0.99307945 0.302998181 -0.098448642 -0.413607068
## [1582,] -1.21741529 0.17971373 -0.929027480 -0.594927874 -0.732313193
## [1583,] -1.27485036 1.04534679 -0.877693077 0.043402567 -0.732313193
## [1584,] -1.21741529 -0.37875921 0.097660571 -0.311225456 -0.286124619
## [1585,] -0.93023990 -1.16062133 0.867676609 -0.098448642 -0.562336593
## [1586,] -0.64306452 -0.76969027 0.867676609 0.043402567 -0.456101218
## [1587,] -0.47075929 -1.21646863 0.713673401 -0.098448642 -0.477348293
## [1588,] -1.44715559 0.45895020 -0.826358675 -0.524002270 -0.456101218
## [1589,] -0.64306452 0.73818667 0.302998181 -0.027523038 -0.413607068
## [1590,] -0.98767498 1.10119409 -0.364349052 3.731534004 -0.307371694
## [1591,] -1.15998021 0.12386643 -0.621021065 -0.524002270 -0.222383394
## [1592,] -1.67689590 1.18496503 -0.929027480 -0.594927874 0.032581506
## [1593,] -1.15998021 -0.09952274 -0.723689870 -0.169374247 -0.243630469
## [1594,] -0.87280483 0.51479750 -0.980361882 -0.453076665 -0.413607068
## [1595,] -1.21741529 0.40310291 -0.980361882 -0.382151061 0.053828581
## [1596,] -1.38972052 0.12386643 -0.877693077 -0.240299852 -0.541089518
## [1597,] -1.15998021 -0.09952274 -0.723689870 -0.169374247 -0.243630469
## [1598,] -1.38972052 0.65441573 -0.775024272 -0.382151061 -0.264877544
## [1599,] -1.33228544 -1.21646863 1.021679816 0.752658612 -0.434854143
## free sulfur dioxide total sulfur dioxide density pH
## [1,] -0.46604672 -0.37901412 0.558099865 1.288239901
## [2,] 0.87236532 0.62416796 0.028251929 -0.719708142
## [3,] -0.08364328 0.22897502 0.134221517 -0.331073037
## [4,] 0.10755844 0.41137176 0.664069453 -0.978798212
## [5,] -0.46604672 -0.37901412 0.558099865 1.288239901
## [6,] -0.27484500 -0.19661738 0.558099865 1.288239901
## [7,] -0.08364328 0.38097231 -0.183687245 -0.071982967
## [8,] -0.08364328 -0.77420706 -1.137413530 0.510969691
## [9,] -0.65724844 -0.86540543 0.028251929 0.316652138
## [10,] 0.10755844 1.68814895 0.558099865 0.251879621
## [11,] -0.08364328 0.56336905 -0.448611213 -0.201528002
## [12,] 0.10755844 1.68814895 0.558099865 0.251879621
## [13,] 0.01195758 0.38097231 -1.296367911 1.741647523
## [14,] -0.65724844 -0.53101140 0.346160691 -0.331073037
## [15,] 3.45358853 2.99532560 0.981978214 -0.978798212
## [16,] 3.35798767 3.08652397 0.981978214 -0.914025694
## [17,] 1.82837392 1.71854841 0.081236723 -0.071982967
## [18,] 0.01195758 0.28977394 0.028251929 -1.302660799
## [19,] -0.94405102 -0.53101140 0.346160691 0.446197173
## [20,] 0.10755844 0.28977394 0.081236723 -1.756068422
## [21,] 1.25476876 0.41137176 0.028251929 0.510969691
## [22,] 0.68116360 0.74576579 0.770039040 1.353012418
## [23,] -0.56164758 -0.28781575 -0.077717658 -0.914025694
## [24,] -0.65724844 0.62416796 0.028251929 -0.914025694
## [25,] 0.48996188 -0.19661738 0.028251929 0.770059761
## [26,] -0.46604672 -0.71340814 -0.660550387 0.187107103
## [27,] -1.13525274 -1.07820163 -0.289656832 -0.201528002
## [28,] -0.56164758 -0.28781575 -0.077717658 -0.914025694
## [29,] -0.17924414 -0.34861466 0.240191104 1.029149831
## [30,] -0.75284930 -0.92620434 -0.183687245 0.446197173
## [31,] 0.10755844 1.08015982 -0.501596007 0.251879621
## [32,] 0.58556274 -0.28781575 -0.077717658 0.964377313
## [33,] -0.08364328 2.02254298 -0.077717658 -0.914025694
## [34,] 2.30637822 1.11055927 1.352871769 0.899604796
## [35,] -0.27484500 0.10737719 -0.554580800 0.446197173
## [36,] -1.03965188 -0.86540543 0.981978214 0.575742208
## [37,] -1.23085360 -0.95660380 0.399145485 0.705287243
## [38,] -0.27484500 -0.50061195 0.028251929 -0.525390589
## [39,] -0.84845016 -0.83500597 -1.455322291 1.223467383
## [40,] -0.37044586 1.23215710 0.558099865 0.122334586
## [41,] -0.37044586 1.23215710 0.558099865 0.122334586
## [42,] 0.10755844 -0.01422063 0.452130278 -0.331073037
## [43,] -0.75284930 -0.98700326 0.028251929 -0.654935624
## [44,] -0.65724844 -0.71340814 0.028251929 -0.071982967
## [45,] -1.03965188 -1.07820163 -0.289656832 1.093922348
## [46,] -0.75284930 0.56336905 -1.773231053 3.814368084
## [47,] 0.58556274 2.05294244 0.134221517 -0.395845554
## [48,] -0.37044586 -0.28781575 0.081236723 -0.395845554
## [49,] -1.03965188 -1.04780217 -0.501596007 0.187107103
## [50,] -0.37044586 1.50575221 -0.713535181 0.057562068
## [51,] -1.13525274 -0.71340814 0.187206310 -1.043570729
## [52,] -0.75284930 -0.95660380 -0.607565594 0.575742208
## [53,] -0.94405102 -0.98700326 -0.660550387 0.510969691
## [54,] 1.35036962 2.20493972 0.134221517 -0.719708142
## [55,] 1.63717220 0.80656470 -0.660550387 -0.914025694
## [56,] 0.87236532 -0.04462009 0.558099865 0.187107103
## [57,] -1.13525274 -1.10860108 0.187206310 -1.756068422
## [58,] 3.26238681 1.93134461 0.823023833 -0.331073037
## [59,] 0.10755844 0.22897502 0.399145485 0.770059761
## [60,] -0.65724844 -0.01422063 -0.289656832 0.640514726
## [61,] 0.29876016 0.16817611 0.664069453 0.834832278
## [62,] 0.39436102 1.99214352 0.028251929 -0.654935624
## [63,] -0.37044586 -0.34861466 0.028251929 0.446197173
## [64,] -0.27484500 0.22897502 -0.077717658 0.510969691
## [65,] -1.13525274 -1.07820163 -0.289656832 0.640514726
## [66,] -1.13525274 -1.07820163 -0.289656832 0.640514726
## [67,] -0.46604672 -0.22701683 0.028251929 0.705287243
## [68,] -0.94405102 -0.95660380 -0.289656832 0.834832278
## [69,] 1.06356704 0.56336905 0.081236723 -0.201528002
## [70,] -0.75284930 -0.83500597 -0.289656832 0.187107103
## [71,] -0.08364328 -0.59181032 -0.024732864 0.057562068
## [72,] 0.10755844 1.50575221 -0.289656832 0.057562068
## [73,] 0.20315930 1.44495330 -0.342641626 -0.007210449
## [74,] -0.46604672 -0.10541900 0.452130278 -0.007210449
## [75,] 1.15916790 1.11055927 0.876008627 -0.201528002
## [76,] -0.65724844 -0.13581846 0.981978214 1.482557453
## [77,] -0.65724844 -0.13581846 0.981978214 1.482557453
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## [155,] 1.25476876 2.50893429 0.293175897 0.705287243
## [156,] 1.15916790 2.47853484 0.293175897 0.705287243
## [157,] 1.25476876 2.50893429 0.293175897 0.705287243
## [158,] 1.15916790 2.47853484 0.293175897 0.705287243
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## [164,] 1.92397478 2.26573864 0.770039040 0.381424656
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## [171,] -1.13525274 -1.16940000 0.240191104 0.316652138
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## [178,] -0.75284930 -0.25741629 0.240191104 1.741647523
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## [182,] 0.68116360 1.93134461 0.240191104 -1.237888282
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## [184,] -0.46604672 0.56336905 0.187206310 1.482557453
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## [186,] 0.96796618 1.17135819 0.187206310 -0.331073037
## [187,] -0.17924414 0.62416796 0.240191104 0.187107103
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## [191,] 0.48996188 2.44813538 0.452130278 -0.525390589
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## [195,] -0.84845016 -0.56141086 -0.183687245 -0.201528002
## [196,] 0.77676446 2.23533918 0.028251929 -0.395845554
## [197,] -0.08364328 0.25937448 0.028251929 0.964377313
## [198,] -0.37044586 -0.59181032 0.717054246 -1.302660799
## [199,] -0.27484500 1.41455384 -2.303078989 1.676875006
## [200,] -0.37044586 -0.47021249 -1.031443943 1.288239901
## [201,] -0.65724844 -0.68300869 0.107729120 -0.590163107
## [202,] 2.21077736 2.99532560 0.399145485 -1.756068422
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## [204,] 0.01195758 -0.22701683 -0.342641626 0.187107103
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## [207,] -0.65724844 -0.56141086 1.405856563 -0.719708142
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## [227,] 1.06356704 1.04976036 -0.183687245 -1.756068422
## [228,] -0.65724844 -0.71340814 0.876008627 0.510969691
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## [272,] -1.13525274 -0.71340814 1.511826150 -0.201528002
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## [285,] 1.54157134 0.74576579 1.803242515 -0.007210449
## [286,] 1.54157134 0.74576579 1.803242515 -0.007210449
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## [289,] 0.39436102 0.07697774 0.452130278 0.187107103
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## [291,] 0.39436102 0.07697774 0.452130278 0.187107103
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## [296,] -1.03965188 -0.59181032 1.776750118 -1.691295904
## [297,] 0.96796618 0.71536633 0.717054246 -0.978798212
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## [333,] 0.48996188 2.05294244 0.293175897 -0.590163107
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## [335,] 0.10755844 -0.25741629 -0.236672039 0.187107103
## [336,] -0.84845016 -0.77420706 1.299886976 -0.914025694
## [337,] -0.94405102 -0.92620434 -1.031443943 0.251879621
## [338,] 1.25476876 0.35057285 0.346160691 -0.007210449
## [339,] 1.15916790 1.59695058 2.147643673 -0.978798212
## [340,] -0.37044586 -0.53101140 1.564810944 -1.302660799
## [341,] -0.56164758 -0.77420706 1.723765325 -1.237888282
## [342,] -1.03965188 -0.86540543 0.240191104 -0.654935624
## [343,] -0.94405102 -0.98700326 0.770039040 -0.071982967
## [344,] -0.94405102 -0.98700326 0.770039040 -0.071982967
## [345,] -0.94405102 -0.43981303 2.041674086 -1.237888282
## [346,] 2.30637822 0.50257013 0.611084659 1.871192558
## [347,] 0.10755844 -0.37901412 -0.660550387 1.741647523
## [348,] -0.94405102 -0.95660380 0.981978214 -1.885613457
## [349,] -0.08364328 -0.01422063 0.611084659 -1.302660799
## [350,] -0.46604672 -0.56141086 1.405856563 0.316652138
## [351,] 0.10755844 -0.37901412 1.935704499 -0.201528002
## [352,] -0.46604672 -0.62220977 1.405856563 0.251879621
## [353,] -0.75284930 -0.83500597 0.346160691 -0.266300519
## [354,] 0.68116360 0.92816253 2.677491609 -0.849253177
## [355,] 2.35417865 3.60331474 -2.938896512 -0.395845554
## [356,] 0.10755844 -0.43981303 -0.660550387 1.547329971
## [357,] 1.25476876 0.25937448 1.776750118 -0.331073037
## [358,] -0.37044586 -0.53101140 0.134221517 -0.460618072
## [359,] -0.56164758 -0.71340814 1.723765325 -1.043570729
## [360,] -0.56164758 -0.16621792 2.253613261 -0.914025694
## [361,] -0.17924414 1.04976036 0.293175897 -0.784480659
## [362,] 0.48996188 0.10737719 0.770039040 0.381424656
## [363,] -0.94405102 -0.28781575 1.299886976 -1.691295904
## [364,] -0.94405102 -0.95660380 1.087947801 -2.079931009
## [365,] -0.84845016 -0.13581846 2.889430784 -1.561750869
## [366,] -0.84845016 -0.77420706 0.611084659 -0.331073037
## [367,] -0.84845016 -0.13581846 2.889430784 -1.561750869
## [368,] -0.46604672 -0.68300869 1.723765325 -0.978798212
## [369,] -0.08364328 0.86736362 1.617795737 -0.849253177
## [370,] -0.94405102 -0.86540543 -0.289656832 -0.719708142
## [371,] 1.82837392 0.50257013 0.399145485 1.676875006
## [372,] -0.46604672 -0.65260923 -0.024732864 0.057562068
## [373,] 0.96796618 -0.01422063 -0.024732864 0.057562068
## [374,] -0.37044586 0.77616525 -0.448611213 -1.691295904
## [375,] -0.94405102 0.01617882 2.465552435 -1.950385974
## [376,] -0.94405102 -0.95660380 0.876008627 -1.950385974
## [377,] 0.29876016 0.01617882 1.882719705 -0.331073037
## [378,] -0.94405102 -0.86540543 -0.289656832 -0.719708142
## [379,] -0.94405102 -0.68300869 1.087947801 -1.302660799
## [380,] 0.77676446 0.41137176 0.611084659 -0.007210449
## [381,] -0.46604672 -0.59181032 0.346160691 -0.654935624
## [382,] 0.10755844 -0.10541900 2.465552435 -1.626523387
## [383,] -0.46604672 -0.59181032 0.346160691 -0.654935624
## [384,] -0.46604672 -0.59181032 0.346160691 -0.654935624
## [385,] 0.68116360 0.22897502 0.664069453 0.705287243
## [386,] -0.46604672 -0.28781575 0.611084659 0.770059761
## [387,] 0.68116360 0.04657828 0.717054246 0.640514726
## [388,] 0.10755844 -0.13581846 0.240191104 -0.007210449
## [389,] 0.68116360 0.19857556 0.717054246 0.770059761
## [390,] 0.01195758 0.07697774 0.770039040 -0.784480659
## [391,] -0.37044586 1.26255656 -2.303078989 1.612102488
## [392,] 0.10755844 -0.10541900 2.465552435 -1.626523387
## [393,] -0.94405102 -0.62220977 0.664069453 -0.849253177
## [394,] -0.27484500 1.47535276 0.134221517 -0.978798212
## [395,] -0.94405102 -0.65260923 1.564810944 -1.820840939
## [396,] -0.84845016 -0.25741629 1.935704499 -0.590163107
## [397,] 4.98320229 2.35693701 1.405856563 1.029149831
## [398,] -0.27484500 0.07697774 1.087947801 -0.849253177
## [399,] -0.27484500 0.07697774 1.087947801 -0.849253177
## [400,] -0.65724844 -0.13581846 -0.236672039 -1.367433317
## [401,] 4.98320229 2.35693701 1.405856563 1.029149831
## [402,] 0.39436102 -0.25741629 -0.978459149 -0.136755484
## [403,] 0.29876016 0.53296959 1.723765325 -1.367433317
## [404,] -0.56164758 -0.16621792 1.405856563 -1.043570729
## [405,] -0.08364328 -0.59181032 0.346160691 -0.331073037
## [406,] -0.46604672 -0.65260923 -0.077717658 0.316652138
## [407,] -0.65724844 -0.56141086 0.717054246 -0.460618072
## [408,] -0.37044586 -0.50061195 1.511826150 -0.849253177
## [409,] -0.94405102 -0.92620434 0.134221517 -0.784480659
## [410,] 0.96796618 0.07697774 0.717054246 -1.691295904
## [411,] 0.96796618 1.20175764 1.034963008 0.446197173
## [412,] 0.68116360 0.95856199 1.034963008 0.446197173
## [413,] -0.08364328 0.92816253 -0.077717658 -0.266300519
## [414,] -0.94405102 -0.83500597 0.981978214 -0.266300519
## [415,] 0.77676446 2.29613809 0.770039040 -0.331073037
## [416,] 1.44597048 2.66093158 2.465552435 0.057562068
## [417,] -0.94405102 -0.80460651 0.134221517 -0.331073037
## [418,] 1.73277306 2.35693701 -0.607565594 0.834832278
## [419,] -0.84845016 -0.80460651 1.511826150 -0.460618072
## [420,] 1.73277306 0.16817611 0.452130278 2.000737593
## [421,] 1.82837392 0.62416796 0.240191104 -0.201528002
## [422,] 1.54157134 0.04657828 -0.819504768 1.353012418
## [423,] -0.08364328 0.41137176 -0.660550387 0.316652138
## [424,] -0.94405102 -0.68300869 0.558099865 -1.043570729
## [425,] -0.08364328 0.41137176 -0.660550387 0.316652138
## [426,] 1.54157134 0.04657828 -0.819504768 1.353012418
## [427,] 0.29876016 0.04657828 -0.978459149 1.158694866
## [428,] -0.94405102 -0.43981303 1.087947801 -0.331073037
## [429,] -0.65724844 -0.50061195 0.558099865 -0.460618072
## [430,] -0.27484500 -0.34861466 1.564810944 -1.367433317
## [431,] -0.94405102 -0.68300869 0.558099865 -1.043570729
## [432,] 0.48996188 0.59376850 0.346160691 -0.395845554
## [433,] -0.37044586 -0.68300869 -0.501596007 -2.015158492
## [434,] -0.94405102 -0.86540543 1.935704499 -0.978798212
## [435,] 0.58556274 0.22897502 1.511826150 -1.043570729
## [436,] -0.94405102 -0.86540543 1.935704499 -0.978798212
## [437,] 2.11517650 0.47217068 -0.501596007 -0.331073037
## [438,] -0.94405102 -0.74380760 0.981978214 -0.914025694
## [439,] 0.58556274 0.22897502 1.511826150 -1.043570729
## [440,] -0.84845016 0.10737719 -0.872489562 -1.496978352
## [441,] -0.94405102 -0.53101140 1.034963008 -2.792428702
## [442,] -0.84845016 -0.59181032 1.087947801 -1.626523387
## [443,] -0.94405102 -0.10541900 3.419278720 -2.339021079
## [444,] -0.46604672 -0.83500597 -0.236672039 -0.525390589
## [445,] -1.03965188 -0.59181032 -1.773231053 1.676875006
## [446,] -0.94405102 -0.47021249 0.981978214 -0.525390589
## [447,] 1.44597048 0.77616525 1.511826150 -1.367433317
## [448,] -0.94405102 -0.92620434 0.028251929 -0.590163107
## [449,] -0.84845016 -0.59181032 -0.024732864 -0.719708142
## [450,] 0.10755844 -0.34861466 1.405856563 -1.367433317
## [451,] 0.10755844 -0.34861466 1.405856563 -1.367433317
## [452,] -0.65724844 -0.62220977 0.611084659 -1.626523387
## [453,] 0.20315930 -0.34861466 0.028251929 0.834832278
## [454,] -1.03965188 -0.74380760 1.617795737 -0.331073037
## [455,] 0.48996188 0.62416796 -0.819504768 1.223467383
## [456,] -0.94405102 -0.83500597 1.087947801 -0.590163107
## [457,] -1.03965188 -0.74380760 0.981978214 0.381424656
## [458,] 1.25476876 0.56336905 1.087947801 -0.201528002
## [459,] -1.03965188 -0.74380760 1.617795737 -0.331073037
## [460,] -0.56164758 0.01617882 2.147643673 -0.395845554
## [461,] -0.17924414 -0.71340814 0.452130278 0.251879621
## [462,] -0.94405102 -0.83500597 0.770039040 -0.331073037
## [463,] -0.56164758 -0.65260923 0.134221517 -0.849253177
## [464,] 0.87236532 2.50893429 0.240191104 -1.496978352
## [465,] -1.03965188 -0.95660380 1.034963008 -2.144703527
## [466,] -0.56164758 -0.62220977 2.041674086 1.093922348
## [467,] 0.48996188 0.13777665 0.770039040 -0.978798212
## [468,] -0.84845016 -0.86540543 -1.084428736 0.057562068
## [469,] -0.94405102 -0.77420706 1.723765325 -0.914025694
## [470,] 1.92397478 0.04657828 -0.819504768 -0.719708142
## [471,] -0.08364328 0.01617882 1.511826150 -1.691295904
## [472,] 0.87236532 0.16817611 0.134221517 -0.719708142
## [473,] 0.87236532 0.65456742 1.458841357 -1.043570729
## [474,] -1.03965188 -0.98700326 0.187206310 -0.331073037
## [475,] -0.56164758 -0.68300869 0.770039040 -0.719708142
## [476,] -1.03965188 -0.56141086 1.087947801 -1.108343247
## [477,] -0.94405102 -0.92620434 0.664069453 -0.201528002
## [478,] -0.94405102 -0.80460651 0.505115072 -0.849253177
## [479,] -1.03965188 -0.56141086 1.087947801 -1.108343247
## [480,] -0.94405102 -0.47021249 0.876008627 -0.784480659
## [481,] -0.94405102 -0.71340814 3.101369958 -1.237888282
## [482,] -0.94405102 -0.89580489 -0.183687245 -1.043570729
## [483,] -0.94405102 -0.86540543 0.981978214 -1.756068422
## [484,] -0.84845016 -0.86540543 0.981978214 -1.756068422
## [485,] -0.94405102 -0.68300869 0.134221517 -1.626523387
## [486,] -0.94405102 -0.89580489 0.452130278 -0.914025694
## [487,] -0.94405102 -0.89580489 0.452130278 -0.914025694
## [488,] -1.03965188 -0.98700326 0.770039040 -0.914025694
## [489,] 1.82837392 0.62416796 1.829734912 0.057562068
## [490,] -0.56164758 -0.62220977 0.876008627 0.187107103
## [491,] 0.77676446 0.28977394 0.876008627 -0.007210449
## [492,] -0.37044586 -0.65260923 -0.819504768 0.187107103
## [493,] -0.27484500 -0.59181032 -0.925474355 0.057562068
## [494,] 0.68116360 1.04976036 1.829734912 1.093922348
## [495,] 1.15916790 1.35375493 -0.819504768 0.834832278
## [496,] -1.03965188 -0.92620434 0.240191104 -1.043570729
## [497,] -0.17924414 -0.25741629 0.876008627 0.770059761
## [498,] 2.59318080 2.02254298 -0.077717658 0.057562068
## [499,] -1.03965188 -0.92620434 0.240191104 -1.043570729
## [500,] 0.68116360 1.04976036 1.829734912 1.093922348
## [501,] -0.17924414 -0.25741629 0.876008627 0.770059761
## [502,] 2.11517650 0.89776307 1.193917389 -0.914025694
## [503,] 2.11517650 0.89776307 1.193917389 -0.914025694
## [504,] -0.94405102 -0.68300869 0.452130278 -0.849253177
## [505,] -0.94405102 -0.74380760 0.452130278 -0.654935624
## [506,] -0.56164758 -0.62220977 0.028251929 -0.978798212
## [507,] -0.94405102 -0.77420706 0.452130278 -0.395845554
## [508,] -0.94405102 -1.01740271 1.723765325 -0.914025694
## [509,] 0.96796618 0.47217068 1.405856563 -0.849253177
## [510,] 0.68116360 -0.10541900 0.981978214 -1.756068422
## [511,] 0.29876016 -0.10541900 0.558099865 -1.885613457
## [512,] 0.96796618 0.47217068 1.405856563 -0.849253177
## [513,] -0.08364328 0.07697774 0.664069453 -1.820840939
## [514,] -0.94405102 -0.95660380 0.293175897 -1.432205834
## [515,] -0.94405102 -0.95660380 0.293175897 -1.432205834
## [516,] 1.73277306 3.17772234 2.253613261 -0.007210449
## [517,] -1.03965188 -0.98700326 2.253613261 -0.395845554
## [518,] -1.03965188 -0.92620434 1.405856563 -0.978798212
## [519,] -0.46604672 -0.43981303 0.240191104 -0.590163107
## [520,] 2.21077736 1.80974678 -0.077717658 0.316652138
## [521,] -0.08364328 -0.40941357 0.770039040 0.705287243
## [522,] 0.01195758 -0.10541900 0.664069453 1.093922348
## [523,] 2.97558423 2.63053212 0.611084659 0.446197173
## [524,] 2.11517650 2.90412723 0.558099865 -0.590163107
## [525,] 0.68116360 2.11374135 0.452130278 -0.525390589
## [526,] 1.25476876 0.19857556 1.617795737 -0.460618072
## [527,] 2.21077736 1.80974678 -0.077717658 0.316652138
## [528,] 1.63717220 1.17135819 -0.289656832 0.510969691
## [529,] 1.54157134 0.25937448 1.087947801 0.187107103
## [530,] -0.94405102 -0.40941357 0.346160691 -1.432205834
## [531,] -1.42205532 -0.56141086 1.193917389 0.640514726
## [532,] -0.37044586 -0.13581846 1.935704499 -0.978798212
## [533,] -0.37044586 -0.13581846 1.935704499 -0.978798212
## [534,] -0.08364328 -0.40941357 -0.607565594 -0.590163107
## [535,] -0.27484500 -0.43981303 1.723765325 -0.201528002
## [536,] -1.42205532 -0.56141086 1.193917389 0.640514726
## [537,] -0.94405102 -0.40941357 0.346160691 -1.432205834
## [538,] -1.03965188 -1.01740271 0.240191104 0.381424656
## [539,] -1.03965188 -0.34861466 2.465552435 -0.719708142
## [540,] -1.03965188 -0.89580489 1.511826150 -0.590163107
## [541,] 0.39436102 0.01617882 1.087947801 -0.331073037
## [542,] -1.03965188 -0.89580489 1.087947801 -0.654935624
## [543,] -1.03965188 -0.80460651 -0.077717658 -1.237888282
## [544,] 0.01195758 0.19857556 1.352871769 -1.626523387
## [545,] -0.94405102 -0.95660380 2.147643673 -2.921973737
## [546,] 2.11517650 1.80974678 0.770039040 -1.496978352
## [547,] -0.56164758 -0.56141086 0.823023833 0.899604796
## [548,] -0.94405102 -0.77420706 1.034963008 -0.331073037
## [549,] 1.06356704 0.68496688 1.405856563 -1.237888282
## [550,] -0.94405102 -0.65260923 0.399145485 -0.266300519
## [551,] -0.65724844 -0.65260923 -0.501596007 0.122334586
## [552,] -0.17924414 -0.04462009 0.664069453 -0.784480659
## [553,] -0.17924414 -0.07501955 0.664069453 -1.237888282
## [554,] 1.54157134 1.50575221 -1.773231053 2.778007803
## [555,] -0.56164758 -0.71340814 3.392786323 -2.533338632
## [556,] -0.56164758 -0.71340814 3.392786323 -2.533338632
## [557,] -0.56164758 -0.89580489 1.829734912 -1.561750869
## [558,] -0.56164758 -0.71340814 3.392786323 -2.533338632
## [559,] -0.56164758 -0.89580489 1.829734912 -1.561750869
## [560,] -0.94405102 0.01617882 2.836445990 -0.071982967
## [561,] -1.03965188 -0.83500597 1.405856563 -1.108343247
## [562,] 0.96796618 2.26573864 0.558099865 -0.525390589
## [563,] 2.40197908 1.93134461 0.770039040 -1.496978352
## [564,] 0.87236532 0.41137176 0.187206310 -0.007210449
## [565,] -0.94405102 0.01617882 2.836445990 -0.071982967
## [566,] -1.03965188 -0.83500597 1.405856563 -1.108343247
## [567,] -1.03965188 -0.95660380 1.246902182 0.057562068
## [568,] -1.03965188 -0.95660380 1.246902182 0.057562068
## [569,] -1.03965188 -0.80460651 1.193917389 -0.007210449
## [570,] 0.29876016 -0.13581846 -1.137413530 1.676875006
## [571,] -0.56164758 -0.28781575 1.882719705 0.057562068
## [572,] 0.29876016 -0.13581846 -1.137413530 1.676875006
## [573,] -0.56164758 -0.56141086 0.558099865 -1.108343247
## [574,] -0.17924414 0.01617882 1.246902182 -0.071982967
## [575,] 0.39436102 0.95856199 1.299886976 -0.784480659
## [576,] 1.15916790 -0.01422063 1.352871769 -0.719708142
## [577,] -0.94405102 -0.98700326 0.558099865 0.187107103
## [578,] 0.20315930 1.96174407 0.770039040 -0.071982967
## [579,] 0.10755844 1.93134461 0.770039040 -0.136755484
## [580,] 0.20315930 -0.19661738 0.452130278 -1.108343247
## [581,] -1.03965188 -0.98700326 1.829734912 -0.784480659
## [582,] -1.03965188 -0.98700326 1.829734912 -0.784480659
## [583,] -1.03965188 -0.95660380 1.723765325 -0.784480659
## [584,] -0.56164758 -0.77420706 0.452130278 -2.144703527
## [585,] 3.64479025 1.01936090 1.829734912 -0.071982967
## [586,] -0.75284930 -0.25741629 0.664069453 1.029149831
## [587,] 0.01195758 0.01617882 0.981978214 -1.237888282
## [588,] 0.20315930 1.68814895 -0.024732864 -0.331073037
## [589,] 0.29876016 0.10737719 -2.673972544 2.648462768
## [590,] -1.03965188 -1.01740271 0.452130278 -1.691295904
## [591,] 0.48996188 0.86736362 1.034963008 0.251879621
## [592,] 0.68116360 3.11692343 -2.409048576 -1.237888282
## [593,] 0.48996188 0.86736362 1.034963008 0.251879621
## [594,] -0.65724844 -0.10541900 1.935704499 -0.136755484
## [595,] 0.39436102 -0.25741629 0.558099865 0.575742208
## [596,] 1.35036962 1.90094515 1.617795737 -0.266300519
## [597,] -0.94405102 -0.68300869 1.405856563 -1.756068422
## [598,] -1.03965188 -0.86540543 0.664069453 -1.432205834
## [599,] -1.03965188 -0.50061195 -0.024732864 -0.719708142
## [600,] -0.46604672 -0.74380760 1.723765325 -2.015158492
## [601,] -0.84845016 -0.71340814 -0.289656832 -0.331073037
## [602,] -0.37044586 -0.34861466 2.041674086 -1.367433317
## [603,] -0.94405102 -0.98700326 0.399145485 -0.071982967
## [604,] -0.37044586 -0.34861466 2.041674086 -1.367433317
## [605,] -0.17924414 0.50257013 0.876008627 -0.914025694
## [606,] -0.94405102 -0.68300869 0.876008627 -0.007210449
## [607,] -0.56164758 -0.80460651 0.293175897 0.187107103
## [608,] 0.96796618 0.16817611 0.770039040 -0.007210449
## [609,] -0.46604672 0.56336905 3.101369958 0.057562068
## [610,] -0.08364328 -0.22701683 -0.607565594 1.612102488
## [611,] 0.87236532 0.32017339 0.823023833 0.510969691
## [612,] -1.03965188 -0.92620434 2.041674086 -2.144703527
## [613,] 0.20315930 -0.53101140 -0.130702451 0.057562068
## [614,] -0.56164758 -0.53101140 -0.289656832 -0.331073037
## [615,] -0.56164758 1.71854841 0.081236723 -2.857201219
## [616,] 1.15916790 0.74576579 1.564810944 1.223467383
## [617,] 1.15916790 0.74576579 1.564810944 1.223467383
## [618,] -0.17924414 -0.56141086 0.770039040 -1.820840939
## [619,] -1.13525274 -0.89580489 2.041674086 -1.173115764
## [620,] -0.94405102 -0.92620434 1.087947801 -1.432205834
## [621,] 0.01195758 1.99214352 0.452130278 -0.266300519
## [622,] -0.17924414 1.74894787 0.452130278 -0.201528002
## [623,] -0.65724844 -0.43981303 0.346160691 -1.173115764
## [624,] 0.48996188 -0.04462009 0.346160691 1.158694866
## [625,] 0.48996188 0.35057285 1.564810944 0.964377313
## [626,] 0.48996188 0.35057285 1.564810944 0.964377313
## [627,] -1.03965188 -0.95660380 1.087947801 0.316652138
## [628,] -1.03965188 -0.95660380 1.087947801 0.316652138
## [629,] -0.84845016 -0.47021249 0.452130278 -0.266300519
## [630,] 0.39436102 1.14095873 -0.183687245 -0.654935624
## [631,] -0.84845016 -0.47021249 0.452130278 -0.266300519
## [632,] -1.03965188 -0.83500597 1.087947801 -0.395845554
## [633,] 0.48996188 -0.10541900 -0.183687245 0.057562068
## [634,] -0.46604672 1.20175764 2.253613261 0.770059761
## [635,] 2.87998338 1.68814895 -0.183687245 -0.266300519
## [636,] 0.77676446 -0.40941357 -0.713535181 -0.266300519
## [637,] 1.35036962 3.05612452 0.611084659 -0.460618072
## [638,] 1.44597048 2.99532560 0.558099865 -0.460618072
## [639,] -0.84845016 -0.71340814 -0.183687245 0.251879621
## [640,] 0.87236532 0.32017339 0.134221517 -0.849253177
## [641,] 0.01195758 -0.19661738 1.246902182 0.510969691
## [642,] 0.48996188 0.65456742 1.299886976 0.964377313
## [643,] 0.01195758 -0.19661738 1.246902182 0.510969691
## [644,] 0.48996188 0.65456742 1.299886976 0.964377313
## [645,] 0.01195758 -0.19661738 1.246902182 0.510969691
## [646,] -1.03965188 -1.07820163 0.876008627 0.381424656
## [647,] -0.94405102 -0.80460651 0.240191104 0.575742208
## [648,] -1.03965188 -0.98700326 -0.024732864 0.057562068
## [649,] -0.56164758 -0.56141086 -0.183687245 0.122334586
## [650,] 0.77676446 3.08652397 -1.031443943 -0.978798212
## [651,] -0.75284930 -0.43981303 0.981978214 -2.727656184
## [652,] 1.82837392 3.29932017 2.253613261 0.640514726
## [653,] 0.58556274 0.74576579 0.452130278 -2.144703527
## [654,] -0.65724844 -0.50061195 0.452130278 -1.237888282
## [655,] -0.84845016 -0.53101140 0.611084659 -1.496978352
## [656,] 0.39436102 0.19857556 1.935704499 -1.108343247
## [657,] -0.75284930 -0.43981303 0.981978214 -2.727656184
## [658,] 0.68116360 -0.13581846 0.664069453 -2.533338632
## [659,] -1.03965188 -0.80460651 0.293175897 0.057562068
## [660,] -1.03965188 -1.01740271 0.134221517 0.640514726
## [661,] -1.03965188 -0.80460651 0.293175897 0.057562068
## [662,] -0.08364328 -0.13581846 0.558099865 -0.007210449
## [663,] -0.65724844 -0.59181032 0.240191104 0.316652138
## [664,] -1.03965188 -1.01740271 0.346160691 -1.756068422
## [665,] -0.08364328 0.22897502 1.723765325 -1.820840939
## [666,] 0.87236532 0.04657828 0.717054246 -1.108343247
## [667,] -0.94405102 -0.92620434 0.664069453 -0.849253177
## [668,] -0.94405102 -0.95660380 1.087947801 -2.403793597
## [669,] -0.08364328 -0.47021249 -0.077717658 -1.043570729
## [670,] -0.94405102 -0.95660380 1.087947801 -2.403793597
## [671,] 1.35036962 -0.04462009 -0.448611213 -0.331073037
## [672,] -1.03965188 -1.01740271 0.028251929 -0.719708142
## [673,] 1.54157134 3.17772234 0.664069453 -1.043570729
## [674,] -1.03965188 -1.01740271 0.028251929 -0.719708142
## [675,] -0.84845016 -0.89580489 0.876008627 -1.496978352
## [676,] -0.37044586 -0.47021249 0.876008627 -0.331073037
## [677,] -0.84845016 -0.89580489 0.876008627 -1.496978352
## [678,] -0.37044586 -0.47021249 0.611084659 0.575742208
## [679,] 2.78438252 1.23215710 0.823023833 1.093922348
## [680,] 0.39436102 0.07697774 0.240191104 -1.173115764
## [681,] -0.08364328 -0.19661738 1.935704499 -1.626523387
## [682,] 0.01195758 -0.53101140 -0.289656832 -0.654935624
## [683,] 1.54157134 0.35057285 0.664069453 0.122334586
## [684,] -1.03965188 -0.95660380 0.134221517 0.381424656
## [685,] 1.82837392 3.20812180 0.664069453 -0.395845554
## [686,] -1.03965188 -0.95660380 0.134221517 0.381424656
## [687,] -0.27484500 -0.19661738 0.134221517 0.834832278
## [688,] -0.27484500 -0.25741629 1.617795737 -0.201528002
## [689,] -1.13525274 -1.13900054 -0.289656832 0.575742208
## [690,] -1.03965188 -0.89580489 0.452130278 -0.071982967
## [691,] -1.03965188 -0.98700326 -0.077717658 2.065510111
## [692,] -0.37044586 1.41455384 1.617795737 1.093922348
## [693,] 0.01195758 0.47217068 0.611084659 -1.820840939
## [694,] 0.48996188 2.29613809 0.876008627 0.057562068
## [695,] 0.77676446 2.38733646 0.876008627 -0.007210449
## [696,] 0.20315930 -0.07501955 -2.462033370 3.814368084
## [697,] -0.75284930 -0.65260923 0.240191104 1.029149831
## [698,] -0.75284930 -0.65260923 0.240191104 1.029149831
## [699,] 0.20315930 0.77616525 1.776750118 -0.007210449
## [700,] -1.03965188 -0.83500597 0.981978214 -1.302660799
## [701,] 0.96796618 1.26255656 0.876008627 -1.496978352
## [702,] -0.75284930 -0.65260923 0.240191104 1.029149831
## [703,] -0.65724844 -0.71340814 0.134221517 1.029149831
## [704,] 0.58556274 1.14095873 0.240191104 0.057562068
## [705,] -1.13525274 -0.98700326 0.664069453 -0.136755484
## [706,] -0.46604672 0.22897502 1.193917389 0.381424656
## [707,] -0.56164758 -0.83500597 -0.607565594 0.575742208
## [708,] 0.01195758 -0.28781575 -0.077717658 0.381424656
## [709,] -0.46604672 -0.34861466 -0.395626419 0.187107103
## [710,] -0.27484500 -0.40941357 0.823023833 -0.266300519
## [711,] 0.48996188 1.14095873 0.928993421 -1.626523387
## [712,] 0.01195758 1.99214352 1.617795737 0.446197173
## [713,] -0.27484500 -0.40941357 1.299886976 0.316652138
## [714,] -0.56164758 0.04657828 0.452130278 0.187107103
## [715,] 0.48996188 0.92816253 0.664069453 -0.201528002
## [716,] -0.27484500 -0.37901412 -0.024732864 -0.136755484
## [717,] -0.56164758 0.04657828 0.452130278 0.187107103
## [718,] -0.37044586 0.07697774 0.028251929 -0.654935624
## [719,] -0.56164758 -0.74380760 0.452130278 -0.590163107
## [720,] 0.10755844 -0.04462009 0.452130278 0.964377313
## [721,] -0.56164758 -0.74380760 0.452130278 -0.590163107
## [722,] 1.82837392 1.80974678 0.770039040 -1.367433317
## [723,] -0.08364328 0.04657828 0.028251929 -0.654935624
## [724,] 1.92397478 2.44813538 -0.130702451 -2.403793597
## [725,] -1.03965188 -1.04780217 -0.501596007 1.482557453
## [726,] -1.03965188 -1.01740271 0.452130278 -0.136755484
## [727,] 0.20315930 0.07697774 1.405856563 0.770059761
## [728,] -1.13525274 -1.07820163 0.134221517 0.964377313
## [729,] -1.13525274 -1.07820163 0.134221517 0.964377313
## [730,] 1.06356704 0.35057285 -0.925474355 1.935965076
## [731,] -0.37044586 -0.28781575 0.770039040 -0.914025694
## [732,] -1.13525274 -0.98700326 1.405856563 0.834832278
## [733,] -0.56164758 -0.83500597 -0.077717658 0.510969691
## [734,] 0.01195758 0.77616525 0.664069453 1.547329971
## [735,] -0.65724844 -0.22701683 1.034963008 1.093922348
## [736,] -0.46604672 -0.74380760 -0.236672039 -0.331073037
## [737,] -0.46604672 -0.74380760 -0.236672039 -0.331073037
## [738,] 0.29876016 0.35057285 1.829734912 1.223467383
## [739,] 1.15916790 1.74894787 0.823023833 -1.367433317
## [740,] 0.29876016 -0.25741629 1.193917389 0.251879621
## [741,] -0.37044586 -0.92620434 -0.130702451 0.899604796
## [742,] 1.15916790 2.81292886 0.600487700 -0.654935624
## [743,] -0.65724844 -0.86540543 0.240191104 0.964377313
## [744,] 0.87236532 1.65774950 1.850928829 -1.173115764
## [745,] 0.48996188 1.65774950 1.776750118 -1.173115764
## [746,] -0.37044586 -0.56141086 0.494518113 1.353012418
## [747,] -0.37044586 0.32017339 0.558099865 -0.071982967
## [748,] 0.01195758 0.65456742 0.568696824 -0.071982967
## [749,] -0.37044586 -0.47021249 0.457428758 1.353012418
## [750,] -0.37044586 -0.56141086 0.494518113 1.353012418
## [751,] 0.10755844 -0.19661738 0.679964891 -0.136755484
## [752,] 0.10755844 -0.19661738 0.679964891 -0.136755484
## [753,] 0.77676446 0.59376850 0.584592262 0.510969691
## [754,] 0.10755844 -0.19661738 0.679964891 -0.136755484
## [755,] -0.17924414 -0.43981303 -0.098911575 -1.432205834
## [756,] 0.58556274 0.01617882 -0.793012371 1.288239901
## [757,] -0.08364328 -0.40941357 -0.989056108 1.871192558
## [758,] -0.56164758 -0.47021249 -0.098911575 -0.395845554
## [759,] -0.56164758 -0.47021249 -0.098911575 -0.395845554
## [760,] 1.63717220 1.26255656 0.785934478 -0.784480659
## [761,] -0.65724844 1.74894787 0.552801386 -0.525390589
## [762,] -1.03965188 -0.34861466 0.335563732 -0.395845554
## [763,] -0.75284930 -0.83500597 0.139519996 -0.007210449
## [764,] -1.03965188 -0.34861466 0.335563732 -0.395845554
## [765,] -0.46604672 -0.07501955 1.130335636 -0.007210449
## [766,] -0.37044586 0.04657828 1.130335636 -0.007210449
## [767,] -0.37044586 0.83696416 0.335563732 -1.108343247
## [768,] -0.27484500 1.56655113 1.395259604 0.899604796
## [769,] 0.77676446 1.44495330 0.367354608 1.547329971
## [770,] -0.84845016 -0.43981303 -0.035329823 0.510969691
## [771,] 0.77676446 1.44495330 0.367354608 1.547329971
## [772,] 0.68116360 2.93452669 0.558099865 -0.201528002
## [773,] 0.68116360 2.96492615 0.568696824 -0.266300519
## [774,] -1.42205532 -0.07501955 0.293175897 -0.071982967
## [775,] -1.32645446 -0.04462009 0.277280459 -0.007210449
## [776,] -0.84845016 -0.92620434 -0.469805131 0.770059761
## [777,] -0.99185145 0.04657828 -0.332044667 0.575742208
## [778,] -0.94405102 -0.86540543 -0.342641626 0.575742208
## [779,] -0.84845016 -0.37901412 0.600487700 0.316652138
## [780,] 0.48996188 1.38415439 0.372653088 1.223467383
## [781,] -0.37044586 -0.04462009 0.007058012 -0.007210449
## [782,] -0.65724844 -0.28781575 0.303772856 2.259827663
## [783,] 0.96796618 1.50575221 0.738248164 0.316652138
## [784,] -0.65724844 -0.28781575 0.303772856 2.259827663
## [785,] 0.39436102 1.17135819 0.377951567 1.547329971
## [786,] -0.46604672 0.44177122 0.770039040 -0.654935624
## [787,] -0.46604672 0.44177122 0.770039040 -0.654935624
## [788,] 0.29876016 0.35057285 1.246902182 -0.849253177
## [789,] 0.29876016 0.35057285 1.246902182 -0.849253177
## [790,] 0.48996188 2.20493972 0.664069453 -1.432205834
## [791,] 0.20315930 1.08015982 0.176609351 0.122334586
## [792,] 0.87236532 2.53933375 0.759442081 -0.525390589
## [793,] 0.10755844 1.23215710 0.372653088 1.093922348
## [794,] -0.84845016 -1.01740271 -0.188985724 0.446197173
## [795,] -0.84845016 -0.62220977 -0.761221495 -0.914025694
## [796,] -0.84845016 0.41137176 0.589890742 -2.079931009
## [797,] 0.77676446 0.53296959 0.377951567 -1.367433317
## [798,] 0.48996188 -0.13581846 -0.787713892 -0.460618072
## [799,] -1.03965188 -0.98700326 1.034963008 -0.136755484
## [800,] -1.03965188 -0.98700326 1.034963008 -0.136755484
## [801,] 0.96796618 1.87054570 -0.178388766 -0.395845554
## [802,] -0.75284930 -0.89580489 0.319668294 -0.525390589
## [803,] -0.17924414 1.20175764 -2.175915484 1.612102488
## [804,] -0.17924414 -0.01422063 0.187206310 -0.460618072
## [805,] -1.13525274 -0.86540543 0.038848888 -0.331073037
## [806,] -1.13525274 -1.10860108 -1.688455383 0.122334586
## [807,] -1.13525274 -1.10860108 -1.529501003 -0.266300519
## [808,] -1.13525274 -1.10860108 -1.688455383 0.122334586
## [809,] -1.13525274 -1.04780217 0.144818475 -0.331073037
## [810,] -1.13525274 -0.95660380 0.097132161 -0.590163107
## [811,] -1.13525274 -0.98700326 -0.596968635 -0.071982967
## [812,] -0.84845016 -0.68300869 1.787347077 -1.432205834
## [813,] 0.39436102 -0.25741629 0.759442081 -0.460618072
## [814,] -1.13525274 -1.19979945 -1.126816571 0.834832278
## [815,] 0.29876016 -0.16621792 1.400558084 -0.654935624
## [816,] 0.39436102 -0.25741629 0.759442081 -0.460618072
## [817,] -0.75284930 -0.50061195 0.876008627 -0.525390589
## [818,] 0.29876016 -0.59181032 -0.687042784 -0.201528002
## [819,] 0.48996188 0.01617882 -0.226075080 -0.136755484
## [820,] -0.65724844 0.38097231 1.596601820 -0.201528002
## [821,] -0.65724844 -0.74380760 -0.363835543 0.187107103
## [822,] 0.01195758 -0.13581846 -2.758748214 2.583690251
## [823,] -0.08364328 -0.31821520 0.293175897 1.935965076
## [824,] -0.08364328 -0.31821520 0.293175897 1.935965076
## [825,] -0.94405102 -0.92620434 0.038848888 -0.460618072
## [826,] -0.08364328 -0.28781575 -0.268462915 0.316652138
## [827,] -1.13525274 -1.16940000 -0.872489562 0.575742208
## [828,] -0.08364328 -0.28781575 -0.268462915 0.316652138
## [829,] 1.73277306 -0.04462009 -1.365248142 0.964377313
## [830,] 0.01195758 -0.68300869 -1.582485796 1.612102488
## [831,] -1.03965188 -1.13900054 -0.226075080 0.446197173
## [832,] 0.01195758 -0.68300869 -1.582485796 1.612102488
## [833,] 1.73277306 0.04657828 0.833620792 0.446197173
## [834,] 1.92397478 0.13777665 0.854814710 0.446197173
## [835,] 0.01195758 -0.71340814 0.102430640 0.057562068
## [836,] 1.06356704 0.25937448 -0.104210055 0.510969691
## [837,] 1.92397478 1.62735004 -3.235611356 -0.331073037
## [838,] 1.92397478 1.62735004 -3.235611356 -0.331073037
## [839,] -0.65724844 -0.56141086 -0.014135906 -0.460618072
## [840,] -0.27484500 -0.62220977 -0.146597890 0.964377313
## [841,] -0.65724844 -0.37901412 0.324966774 -0.460618072
## [842,] 0.48996188 -0.47021249 -0.241970518 0.899604796
## [843,] 1.73277306 0.65456742 0.176609351 -0.525390589
## [844,] -0.65724844 1.38415439 -0.236672039 -0.201528002
## [845,] 0.96796618 -0.13581846 -0.448611213 -0.849253177
## [846,] -0.17924414 -0.47021249 0.075938244 1.806420041
## [847,] -0.17924414 -0.47021249 0.075938244 1.806420041
## [848,] -0.37044586 -0.22701683 0.505115072 1.223467383
## [849,] -0.17924414 -0.47021249 0.075938244 1.806420041
## [850,] -0.37044586 -0.43981303 0.075938244 1.741647523
## [851,] -0.65724844 -0.74380760 0.176609351 -0.201528002
## [852,] -0.65724844 -0.74380760 0.176609351 -0.201528002
## [853,] 0.96796618 2.29613809 0.669367932 -0.590163107
## [854,] 2.40197908 0.25937448 -0.120105493 1.029149831
## [855,] 2.40197908 0.25937448 -0.120105493 1.029149831
## [856,] -0.56164758 -0.98700326 -0.724132140 1.288239901
## [857,] 2.40197908 0.25937448 -0.120105493 1.029149831
## [858,] 0.01195758 0.01617882 -0.427417296 0.575742208
## [859,] 0.10755844 -0.43981303 0.060042806 -1.043570729
## [860,] -0.08364328 -0.68300869 -1.253980076 0.575742208
## [861,] -0.08364328 1.17135819 0.377951567 1.288239901
## [862,] -0.08364328 1.26255656 -1.683156904 2.259827663
## [863,] -0.84845016 -0.65260923 -0.247268997 -0.460618072
## [864,] 0.10755844 1.14095873 0.377951567 1.288239901
## [865,] -0.08364328 1.17135819 0.377951567 1.288239901
## [866,] 0.01195758 1.20175764 0.388548526 1.288239901
## [867,] -0.27484500 -0.68300869 -1.253980076 0.640514726
## [868,] -0.27484500 -0.74380760 -1.253980076 0.575742208
## [869,] -0.08364328 -0.68300869 -1.253980076 0.575742208
## [870,] 1.06356704 -0.10541900 -0.512192965 0.834832278
## [871,] 1.44597048 -0.10541900 -1.608978193 0.640514726
## [872,] 1.92397478 -0.01422063 -0.808907810 1.417784936
## [873,] 1.15916790 0.04657828 -0.522789924 0.770059761
## [874,] -0.94405102 -1.10860108 -0.649953429 -0.525390589
## [875,] -0.94405102 -1.10860108 -0.056523740 -1.237888282
## [876,] -0.84845016 -0.92620434 -0.321447708 -0.007210449
## [877,] -0.84845016 -0.98700326 -0.835400206 0.575742208
## [878,] 1.44597048 -0.10541900 -1.608978193 0.640514726
## [879,] 1.35036962 0.68496688 0.595189221 -0.590163107
## [880,] 0.20315930 1.26255656 0.372653088 1.417784936
## [881,] -0.56164758 -0.77420706 -0.522789924 -1.043570729
## [882,] 1.35036962 -0.34861466 -0.750624537 1.093922348
## [883,] -0.17924414 -0.62220977 -0.734729099 -0.590163107
## [884,] 0.20315930 1.26255656 0.372653088 1.417784936
## [885,] 1.35036962 0.68496688 0.595189221 -0.590163107
## [886,] -0.65724844 -0.50061195 0.791232957 0.187107103
## [887,] -0.75284930 -0.56141086 0.854814710 0.122334586
## [888,] 1.25476876 0.28977394 -0.517491445 -1.043570729
## [889,] 1.54157134 0.53296959 -0.973160670 0.770059761
## [890,] 0.68116360 1.59695058 3.255025860 -0.590163107
## [891,] 1.35036962 0.16817611 -0.522789924 0.834832278
## [892,] 0.01195758 1.20175764 0.362056129 1.417784936
## [893,] -0.56164758 -0.86540543 0.526308989 -0.590163107
## [894,] 0.01195758 1.20175764 0.362056129 1.417784936
## [895,] 0.10755844 1.26255656 0.372653088 1.417784936
## [896,] 1.06356704 -0.10541900 -0.660550387 0.705287243
## [897,] 0.10755844 -0.10541900 -1.222189200 -0.007210449
## [898,] 1.06356704 -0.10541900 -0.660550387 0.705287243
## [899,] 0.10755844 -0.10541900 -1.222189200 -0.007210449
## [900,] -0.94405102 -1.07820163 1.151529554 1.093922348
## [901,] -0.56164758 -0.22701683 -0.596968635 0.575742208
## [902,] 0.01195758 -0.40941357 0.324966774 1.741647523
## [903,] 0.01195758 -0.40941357 0.324966774 1.741647523
## [904,] -0.46604672 -0.86540543 -0.289656832 0.640514726
## [905,] -0.46604672 -0.86540543 -0.289656832 0.640514726
## [906,] -0.08364328 2.08334189 0.664069453 -0.525390589
## [907,] -0.37044586 0.95856199 -0.183687245 0.510969691
## [908,] -0.94405102 -1.13900054 -1.031443943 1.806420041
## [909,] 1.73277306 0.44177122 -0.777116933 0.770059761
## [910,] -0.84845016 -1.07820163 -1.820917367 -0.136755484
## [911,] -1.23085360 -1.07820163 -0.517491445 -0.525390589
## [912,] -1.23085360 -1.13900054 1.199215868 -0.849253177
## [913,] -1.13525274 -1.16940000 -0.003538947 0.122334586
## [914,] -1.23085360 -1.10860108 -0.188985724 -0.266300519
## [915,] -0.84845016 -1.07820163 -1.820917367 -0.136755484
## [916,] -1.23085360 -1.23019891 -0.861892603 -0.266300519
## [917,] -0.84845016 0.47217068 -1.481814688 2.000737593
## [918,] 0.96796618 -0.04462009 0.791232957 0.446197173
## [919,] 1.54157134 0.98896144 -0.183687245 -0.071982967
## [920,] 2.11517650 -0.01422063 -0.904280438 0.446197173
## [921,] -0.56164758 -0.71340814 0.589890742 -0.460618072
## [922,] 1.54157134 0.98896144 -0.183687245 -0.071982967
## [923,] 2.11517650 -0.01422063 -0.904280438 0.446197173
## [924,] 0.96796618 -0.04462009 0.791232957 0.446197173
## [925,] -0.17924414 -0.56141086 -0.840698686 -0.849253177
## [926,] 3.54918939 0.92816253 -0.374432502 1.029149831
## [927,] 3.45358853 0.80656470 0.589890742 1.029149831
## [928,] -0.46604672 0.86736362 0.324966774 -0.719708142
## [929,] -0.17924414 -0.56141086 -0.840698686 -0.849253177
## [930,] -0.56164758 -0.83500597 -1.095025695 -0.071982967
## [931,] -0.75284930 -0.65260923 0.377951567 2.454145216
## [932,] -0.27484500 -0.25741629 0.388548526 1.093922348
## [933,] 1.25476876 0.59376850 0.187206310 0.899604796
## [934,] -0.27484500 -0.25741629 0.388548526 1.093922348
## [935,] -0.75284930 -0.65260923 0.377951567 2.454145216
## [936,] 0.29876016 0.77616525 -0.697639743 0.510969691
## [937,] 0.29876016 0.77616525 -0.697639743 0.510969691
## [938,] -0.94405102 -0.62220977 0.616383138 -1.561750869
## [939,] 0.68116360 -0.13581846 -1.688455383 0.187107103
## [940,] -0.84845016 -1.07820163 -1.322860307 1.935965076
## [941,] -0.27484500 -0.65260923 -0.877788041 0.316652138
## [942,] -0.65724844 -0.89580489 -1.010250025 -0.784480659
## [943,] -0.27484500 0.16817611 0.844217751 -0.590163107
## [944,] -0.56164758 -0.04462009 1.003172132 -0.460618072
## [945,] -0.46604672 -0.68300869 -0.936071314 -0.266300519
## [946,] -0.84845016 -0.80460651 -0.575774718 -1.108343247
## [947,] -0.46604672 -0.68300869 0.372653088 -0.136755484
## [948,] -0.94405102 -1.04780217 -1.412934457 -0.331073037
## [949,] -0.56164758 -0.77420706 -0.649953429 0.640514726
## [950,] -0.56164758 -0.77420706 -0.649953429 0.640514726
## [951,] -0.56164758 -0.77420706 -0.649953429 0.640514726
## [952,] -0.94405102 -1.04780217 -1.412934457 -0.331073037
## [953,] -0.94405102 -1.10860108 -0.734729099 -0.007210449
## [954,] -1.03965188 -1.13900054 -1.148010489 -0.719708142
## [955,] -0.94405102 -1.07820163 -0.724132140 0.834832278
## [956,] -0.65724844 -0.71340814 -0.141299410 0.316652138
## [957,] -1.03965188 -1.10860108 -0.565177759 -0.007210449
## [958,] -0.37044586 0.13777665 -0.326746188 -0.136755484
## [959,] 0.87236532 -0.31821520 -0.824803248 1.029149831
## [960,] -0.37044586 -0.43981303 0.319668294 0.316652138
## [961,] 0.20315930 -0.25741629 -0.830101727 -0.978798212
## [962,] -0.84845016 -0.86540543 -0.438014254 -0.266300519
## [963,] -0.94405102 -0.92620434 -0.109508534 0.446197173
## [964,] 0.39436102 -0.59181032 -0.681744305 -1.043570729
## [965,] 0.20315930 -0.25741629 -0.830101727 -0.978798212
## [966,] 0.77676446 0.04657828 -0.639356470 0.187107103
## [967,] -0.94405102 -1.10860108 -0.374432502 -1.173115764
## [968,] 0.68116360 2.02254298 0.309071335 -1.173115764
## [969,] 1.25476876 -0.01422063 -1.296367911 -0.719708142
## [970,] -0.84845016 -0.74380760 -0.030031344 0.964377313
## [971,] -0.94405102 -1.10860108 0.261385021 0.122334586
## [972,] -0.94405102 -1.10860108 0.261385021 0.122334586
## [973,] -0.94405102 -1.01740271 -0.167791807 -0.590163107
## [974,] -0.94405102 -1.04780217 -0.369134023 1.353012418
## [975,] -0.84845016 -1.01740271 -0.088314617 -0.071982967
## [976,] 1.82837392 0.77616525 0.134221517 0.834832278
## [977,] 1.82837392 0.77616525 0.134221517 0.834832278
## [978,] 1.44597048 2.20493972 0.669367932 -1.043570729
## [979,] -0.65724844 -0.53101140 -1.370546622 -0.201528002
## [980,] -1.23085360 -1.23019891 0.081236723 -1.173115764
## [981,] -0.75284930 -0.89580489 0.526308989 0.057562068
## [982,] -0.27484500 -0.56141086 0.197803269 -0.395845554
## [983,] 3.35798767 0.71536633 -1.359949663 0.187107103
## [984,] -0.75284930 -0.89580489 0.526308989 0.057562068
## [985,] -1.23085360 -1.23019891 0.081236723 -1.173115764
## [986,] -0.84845016 -1.07820163 -0.596968635 0.899604796
## [987,] -1.23085360 -1.19979945 -1.084428736 -0.784480659
## [988,] 1.82837392 0.71536633 0.097132161 0.834832278
## [989,] 0.29876016 -0.59181032 -0.416820337 -0.978798212
## [990,] -0.17924414 -0.77420706 -0.628759511 -1.108343247
## [991,] 0.29876016 -0.59181032 -0.416820337 -0.978798212
## [992,] 1.44597048 0.65456742 0.102430640 0.899604796
## [993,] 1.35036962 0.01617882 -0.639356470 0.316652138
## [994,] 1.44597048 0.65456742 0.102430640 0.899604796
## [995,] 0.39436102 1.26255656 1.289290017 -1.043570729
## [996,] 0.29876016 -0.16621792 0.118326078 0.510969691
## [997,] -1.23085360 -1.07820163 -1.571888837 2.583690251
## [998,] -1.23085360 -1.07820163 -1.571888837 2.583690251
## [999,] -1.13525274 -1.10860108 -0.639356470 -1.237888282
## [1000,] -0.84845016 -1.04780217 -2.716360379 1.029149831
## [1001,] -0.94405102 -1.04780217 -0.951966752 0.834832278
## [1002,] 1.44597048 0.01617882 0.007058012 -0.331073037
## [1003,] -0.27484500 -0.59181032 -0.840698686 -0.331073037
## [1004,] -1.13525274 -1.16940000 -2.091139815 0.316652138
## [1005,] 1.06356704 -0.04462009 -0.379730981 -0.395845554
## [1006,] -1.13525274 -1.16940000 -2.091139815 0.316652138
## [1007,] -0.27484500 -0.59181032 -0.840698686 -0.331073037
## [1008,] -0.37044586 -0.65260923 -0.840698686 -0.331073037
## [1009,] -0.37044586 -0.68300869 -0.665848867 -0.525390589
## [1010,] 0.96796618 0.25937448 0.250788062 -0.849253177
## [1011,] -0.84845016 -1.04780217 -1.699052342 -0.395845554
## [1012,] -0.37044586 -0.83500597 -0.554580800 -0.914025694
## [1013,] -0.46604672 -0.43981303 -0.374432502 -0.525390589
## [1014,] 0.58556274 -0.47021249 -0.210179642 0.446197173
## [1015,] -1.23085360 -1.19979945 -1.169204406 -0.590163107
## [1016,] -0.17924414 -0.59181032 -0.406223378 -1.626523387
## [1017,] -0.37044586 -0.56141086 -0.999653067 -0.266300519
## [1018,] 1.92397478 1.90094515 -3.537624680 -2.727656184
## [1019,] 1.92397478 1.90094515 -3.537624680 -2.727656184
## [1020,] -0.56164758 -0.71340814 -0.204881162 1.417784936
## [1021,] -1.23085360 -1.16940000 -0.173090286 -0.719708142
## [1022,] -1.23085360 -1.16940000 -0.173090286 -0.719708142
## [1023,] -1.13525274 -1.13900054 -0.480402089 0.251879621
## [1024,] -1.23085360 -1.13900054 -0.893683479 -0.266300519
## [1025,] -0.37044586 -0.86540543 -0.565177759 0.057562068
## [1026,] 0.10755844 -0.10541900 0.780635998 0.122334586
## [1027,] -0.17924414 -0.31821520 -1.646067548 0.640514726
## [1028,] 1.15916790 0.16817611 -1.571888837 1.158694866
## [1029,] 2.40197908 0.65456742 -0.469805131 0.381424656
## [1030,] -0.37044586 -0.86540543 -0.565177759 0.057562068
## [1031,] -0.65724844 -0.98700326 -0.989056108 0.705287243
## [1032,] -0.65724844 -0.95660380 -0.851295644 0.251879621
## [1033,] -1.03965188 -0.98700326 0.950187338 0.316652138
## [1034,] -0.17924414 -0.59181032 -0.438014254 -0.071982967
## [1035,] -0.08364328 0.04657828 0.340862212 -0.719708142
## [1036,] -1.03965188 -0.89580489 0.044147367 -0.914025694
## [1037,] 0.96796618 -0.34861466 -1.688455383 -0.590163107
## [1038,] 0.39436102 0.28977394 -0.014135906 0.251879621
## [1039,] 0.87236532 -0.13581846 -0.766519975 -0.460618072
## [1040,] 0.48996188 -0.22701683 0.091833682 0.122334586
## [1041,] 0.01195758 -0.43981303 0.430936361 1.741647523
## [1042,] -0.27484500 -0.62220977 -0.676445826 0.446197173
## [1043,] 0.48996188 -0.22701683 0.091833682 0.122334586
## [1044,] 0.20315930 -0.22701683 0.976679735 -0.136755484
## [1045,] -0.37044586 0.19857556 -2.016961104 0.316652138
## [1046,] 1.54157134 -0.25741629 -1.253980076 0.057562068
## [1047,] 1.63717220 -0.04462009 -0.332044667 -0.007210449
## [1048,] 1.06356704 0.13777665 -0.215478121 1.158694866
## [1049,] -1.23085360 -0.98700326 0.144818475 -0.266300519
## [1050,] -0.94405102 -0.89580489 0.155415434 -0.266300519
## [1051,] 1.06356704 0.13777665 -0.215478121 1.158694866
## [1052,] 0.01195758 -0.04462009 0.144818475 -1.820840939
## [1053,] 0.29876016 -0.65260923 -2.207706361 1.612102488
## [1054,] -0.65724844 -0.80460651 -1.317561828 0.446197173
## [1055,] -1.03965188 0.92816253 0.383250046 -1.173115764
## [1056,] -1.03965188 0.92816253 0.383250046 -1.173115764
## [1057,] -1.23085360 -1.10860108 -0.469805131 -0.654935624
## [1058,] 1.15916790 1.32335547 0.579293783 -1.043570729
## [1059,] 1.35036962 0.16817611 0.187206310 -0.784480659
## [1060,] -1.23085360 -1.10860108 -0.469805131 -0.654935624
## [1061,] -1.23085360 -1.16940000 0.717054246 -1.108343247
## [1062,] -0.84845016 -0.92620434 -1.126816571 -0.654935624
## [1063,] -0.94405102 -0.95660380 -0.607565594 -0.071982967
## [1064,] -0.94405102 -0.89580489 -0.581073197 -0.590163107
## [1065,] -1.03965188 -1.10860108 -1.359949663 -0.201528002
## [1066,] -0.94405102 -0.80460651 -0.236672039 -0.136755484
## [1067,] -0.27484500 -0.62220977 -1.677858425 0.575742208
## [1068,] -1.23085360 -1.10860108 -0.543983842 -1.885613457
## [1069,] -1.23085360 -1.10860108 -0.543983842 -1.885613457
## [1070,] 1.15916790 0.16817611 0.134221517 -0.007210449
## [1071,] 0.29876016 -0.28781575 -0.936071314 -0.849253177
## [1072,] 1.35036962 1.38415439 1.151529554 -0.719708142
## [1073,] 1.63717220 0.86736362 0.028251929 0.575742208
## [1074,] 0.01195758 -0.28781575 0.240191104 -0.007210449
## [1075,] 1.35036962 1.38415439 1.151529554 -0.719708142
## [1076,] 2.78438252 0.62416796 0.499816592 0.834832278
## [1077,] -1.23085360 -1.16940000 -0.745326057 -1.043570729
## [1078,] -1.23085360 -1.16940000 0.754143602 -0.266300519
## [1079,] -1.23085360 -1.16940000 0.754143602 -0.266300519
## [1080,] 2.06737607 7.03845338 -1.900394558 -1.950385974
## [1081,] -1.23085360 -1.16940000 -1.079130257 -0.978798212
## [1082,] 2.06737607 7.37284740 -1.900394558 -1.950385974
## [1083,] 1.44597048 0.71536633 0.054744326 0.705287243
## [1084,] 1.54157134 0.38097231 -0.305552270 0.122334586
## [1085,] 1.44597048 0.71536633 0.054744326 0.705287243
## [1086,] 2.30637822 0.53296959 -0.771818454 -1.237888282
## [1087,] -1.23085360 -1.13900054 0.335563732 0.446197173
## [1088,] 0.20315930 -0.50061195 -1.455322291 -0.136755484
## [1089,] 0.58556274 -0.16621792 0.319668294 -1.885613457
## [1090,] 0.58556274 -0.16621792 0.319668294 -1.885613457
## [1091,] 2.49757994 0.83696416 -1.185099844 -2.144703527
## [1092,] -0.75284930 -0.83500597 -0.681744305 0.251879621
## [1093,] -0.56164758 -0.92620434 -1.036742422 0.770059761
## [1094,] -0.46604672 -0.47021249 -0.316149229 0.122334586
## [1095,] -0.65724844 -0.89580489 -0.104210055 0.251879621
## [1096,] -0.94405102 -0.80460651 0.515712030 -1.043570729
## [1097,] -0.65724844 -0.89580489 -0.104210055 0.251879621
## [1098,] -1.03965188 -0.86540543 -0.045926782 -0.719708142
## [1099,] -1.03965188 -0.92620434 -1.677858425 -1.043570729
## [1100,] -1.03965188 -0.86540543 -0.045926782 -0.719708142
## [1101,] 0.68116360 -0.31821520 -1.497710126 -1.302660799
## [1102,] -0.17924414 -0.65260923 -1.518904044 0.251879621
## [1103,] 0.20315930 -0.50061195 -1.444725333 0.899604796
## [1104,] -0.17924414 -0.65260923 -1.518904044 0.251879621
## [1105,] 0.01195758 -0.65260923 -1.667261466 -0.201528002
## [1106,] -0.27484500 0.07697774 -1.593082755 0.640514726
## [1107,] -0.65724844 -0.89580489 -1.582485796 -0.654935624
## [1108,] -0.56164758 -0.68300869 -0.803609330 -0.266300519
## [1109,] -1.13525274 -1.01740271 0.955485817 0.316652138
## [1110,] 1.06356704 0.59376850 0.770039040 -0.914025694
## [1111,] -0.94405102 -0.98700326 -0.432715775 -0.395845554
## [1112,] 0.68116360 0.25937448 -1.079130257 3.037097873
## [1113,] -0.94405102 -0.34861466 -1.476516209 -0.136755484
## [1114,] -1.23085360 -1.10860108 0.123624558 -1.237888282
## [1115,] 1.25476876 1.01936090 -3.468744448 1.158694866
## [1116,] -0.08364328 -0.77420706 -0.543983842 0.446197173
## [1117,] -0.08364328 -0.77420706 -0.543983842 0.446197173
## [1118,] -0.08364328 -0.77420706 -0.543983842 0.446197173
## [1119,] -0.17924414 -0.68300869 -2.239497237 -0.071982967
## [1120,] -0.84845016 -0.95660380 -2.218303319 1.353012418
## [1121,] -0.75284930 -0.89580489 -2.329571386 -0.719708142
## [1122,] 0.48996188 -0.56141086 -1.709649301 0.834832278
## [1123,] 1.06356704 -0.40941357 -2.409048576 0.899604796
## [1124,] 0.10755844 -0.53101140 -0.003538947 -1.237888282
## [1125,] -1.23085360 -1.01740271 -0.623461032 2.000737593
## [1126,] -0.27484500 -0.59181032 -1.487113168 -1.108343247
## [1127,] -1.23085360 -1.07820163 -2.779942131 0.510969691
## [1128,] 0.96796618 0.16817611 -1.566590358 1.288239901
## [1129,] 1.15916790 1.29295601 0.876008627 -0.590163107
## [1130,] 0.77676446 0.71536633 0.653472494 -0.654935624
## [1131,] -1.03965188 -1.10860108 0.505115072 -0.849253177
## [1132,] 3.93159283 2.69133103 -1.767932574 0.057562068
## [1133,] 0.20315930 -0.25741629 -1.826215847 0.446197173
## [1134,] -0.56164758 -0.86540543 0.049445847 0.381424656
## [1135,] -0.94405102 -0.95660380 -0.798310851 -0.071982967
## [1136,] 0.58556274 0.10737719 -1.137413530 0.446197173
## [1137,] -0.94405102 -1.01740271 0.526308989 -0.590163107
## [1138,] -0.94405102 -1.01740271 0.526308989 -0.590163107
## [1139,] 1.25476876 1.44495330 0.589890742 -1.108343247
## [1140,] 0.01195758 1.20175764 0.473324196 0.187107103
## [1141,] 1.63717220 0.98896144 0.081236723 0.640514726
## [1142,] 0.77676446 0.74576579 -0.268462915 -0.266300519
## [1143,] -0.94405102 -1.04780217 -1.699052342 -0.071982967
## [1144,] 0.01195758 -0.80460651 -0.014135906 1.935965076
## [1145,] 1.82837392 0.71536633 -0.459208172 0.770059761
## [1146,] 1.44597048 0.13777665 -0.014135906 1.417784936
## [1147,] -0.94405102 -0.77420706 -0.395626419 -0.201528002
## [1148,] -0.94405102 -0.98700326 0.144818475 -0.654935624
## [1149,] -0.65724844 -0.77420706 -0.787713892 0.510969691
## [1150,] -0.94405102 -1.07820163 -0.475103610 -0.525390589
## [1151,] -1.13525274 -1.04780217 -1.068533298 -0.071982967
## [1152,] 0.01195758 0.71536633 -1.709649301 0.964377313
## [1153,] -0.65724844 -0.25741629 -0.310850750 -1.043570729
## [1154,] 0.10755844 -0.25741629 -0.279059874 -0.525390589
## [1155,] 3.26238681 0.50257013 -0.692341264 1.806420041
## [1156,] -0.65724844 -0.25741629 -0.310850750 -1.043570729
## [1157,] 2.78438252 1.26255656 -0.798310851 0.122334586
## [1158,] 0.01195758 1.65774950 -2.303078989 0.964377313
## [1159,] 0.58556274 0.22897502 -0.543983842 0.705287243
## [1160,] -0.46604672 -0.28781575 0.282578939 -0.978798212
## [1161,] -0.94405102 -0.80460651 0.007058012 -1.108343247
## [1162,] -0.37044586 -0.77420706 -0.655251908 -0.654935624
## [1163,] -0.37044586 -0.83500597 -1.275173993 -1.108343247
## [1164,] -0.56164758 -0.77420706 0.091833682 -0.136755484
## [1165,] -0.56164758 -0.77420706 0.091833682 -0.136755484
## [1166,] -0.08364328 -0.25741629 -0.215478121 -1.950385974
## [1167,] -0.84845016 0.41137176 0.181907831 -2.403793597
## [1168,] 1.25476876 0.04657828 -0.777116933 0.057562068
## [1169,] -0.75284930 -0.07501955 -1.540097961 -0.654935624
## [1170,] -1.03965188 -0.98700326 -0.914877397 0.057562068
## [1171,] -1.03965188 -1.04780217 -0.040628302 -0.719708142
## [1172,] 0.96796618 -0.07501955 -0.808907810 0.705287243
## [1173,] -1.03965188 -0.92620434 -0.136000931 -0.266300519
## [1174,] 0.96796618 0.56336905 0.218997186 0.964377313
## [1175,] 0.96796618 0.56336905 0.218997186 0.964377313
## [1176,] 3.07118509 0.38097231 -0.708236702 1.935965076
## [1177,] 0.68116360 0.01617882 -0.543983842 1.741647523
## [1178,] -0.94405102 -1.07820163 -1.889797599 0.251879621
## [1179,] 0.10755844 0.95856199 -1.741440177 2.389372698
## [1180,] -0.46604672 0.01617882 -0.400924899 -0.266300519
## [1181,] -0.46604672 0.01617882 -0.400924899 -0.266300519
## [1182,] -1.03965188 -1.07820163 -1.042040901 -0.784480659
## [1183,] 2.40197908 0.22897502 0.420339402 0.446197173
## [1184,] 0.01195758 0.44177122 -0.543983842 -0.136755484
## [1185,] -0.46604672 2.20493972 -0.724132140 0.316652138
## [1186,] -0.94405102 -0.22701683 -1.741440177 0.122334586
## [1187,] -0.94405102 -1.04780217 -0.236672039 1.353012418
## [1188,] -0.94405102 -0.22701683 -1.741440177 0.122334586
## [1189,] -0.46604672 2.20493972 -0.724132140 0.316652138
## [1190,] -1.03965188 -0.83500597 -0.310850750 -0.071982967
## [1191,] -0.94405102 -0.80460651 -0.120105493 -0.201528002
## [1192,] -0.94405102 -1.04780217 -0.655251908 1.612102488
## [1193,] -0.46604672 -0.16621792 -1.248681596 1.353012418
## [1194,] -0.94405102 -1.04780217 -0.655251908 1.612102488
## [1195,] -0.08364328 0.53296959 -0.459208172 -0.590163107
## [1196,] 0.48996188 0.28977394 -0.220776601 1.353012418
## [1197,] 0.68116360 1.44495330 0.060042806 -0.654935624
## [1198,] -1.13525274 -1.13900054 -1.148010489 -0.978798212
## [1199,] 0.29876016 0.92816253 -0.872489562 -1.043570729
## [1200,] 0.68116360 1.44495330 0.060042806 -0.654935624
## [1201,] -1.13525274 -1.13900054 -1.148010489 -0.978798212
## [1202,] -1.03965188 -1.10860108 -1.354651184 -0.266300519
## [1203,] -0.94405102 -1.04780217 -0.840698686 0.251879621
## [1204,] 1.63717220 0.89776307 1.077350843 -1.108343247
## [1205,] 0.77676446 -0.07501955 -0.745326057 0.575742208
## [1206,] 0.77676446 -0.07501955 -0.745326057 0.575742208
## [1207,] 0.77676446 -0.07501955 -0.745326057 0.575742208
## [1208,] 0.77676446 0.16817611 0.409742443 0.251879621
## [1209,] 0.77676446 -0.07501955 -0.745326057 0.575742208
## [1210,] -0.17924414 -0.68300869 -1.306964869 0.899604796
## [1211,] -0.75284930 -0.95660380 -0.936071314 0.251879621
## [1212,] 0.58556274 0.19857556 -0.554580800 0.057562068
## [1213,] -0.75284930 -0.95660380 -0.936071314 0.251879621
## [1214,] -1.03965188 -1.01740271 -0.083016137 -1.043570729
## [1215,] -0.94405102 -1.13900054 -0.247268997 -1.367433317
## [1216,] 0.39436102 -0.53101140 -0.989056108 -0.331073037
## [1217,] -0.56164758 0.98896144 0.012356491 -0.136755484
## [1218,] 2.59318080 0.83696416 -1.412934457 -0.525390589
## [1219,] -0.75284930 -0.68300869 -0.395626419 -0.460618072
## [1220,] 0.87236532 0.10737719 -1.042040901 -0.719708142
## [1221,] 0.10755844 -0.07501955 0.314369815 -0.201528002
## [1222,] 0.10755844 -0.07501955 0.314369815 -0.201528002
## [1223,] 1.63717220 1.29295601 0.017654971 -0.331073037
## [1224,] -0.65724844 -0.71340814 -0.194284204 -0.525390589
## [1225,] -0.75284930 -0.80460651 1.299886976 -1.561750869
## [1226,] 0.20315930 0.92816253 1.310483934 -1.043570729
## [1227,] 1.06356704 -0.01422063 -0.438014254 -1.885613457
## [1228,] -0.75284930 -0.77420706 0.499816592 -0.266300519
## [1229,] 0.20315930 1.26255656 -2.742852776 2.389372698
## [1230,] 0.29876016 0.59376850 0.229594145 0.575742208
## [1231,] -0.08364328 0.35057285 -1.084428736 0.381424656
## [1232,] 3.07118509 1.32335547 -0.284358353 0.446197173
## [1233,] 0.29876016 0.59376850 0.229594145 0.575742208
## [1234,] -0.17924414 -0.31821520 -0.321447708 -0.525390589
## [1235,] -0.46604672 -0.86540543 -2.292482030 0.510969691
## [1236,] -0.94405102 2.02254298 -0.543983842 -0.071982967
## [1237,] -0.84845016 -0.89580489 -0.083016137 -0.331073037
## [1238,] -0.46604672 -0.86540543 -2.292482030 0.510969691
## [1239,] -0.37044586 -0.68300869 0.653472494 0.446197173
## [1240,] -0.46604672 -0.80460651 -0.989056108 0.899604796
## [1241,] 1.92397478 0.41137176 -0.957265232 -1.367433317
## [1242,] 1.15916790 0.56336905 0.287877418 -0.978798212
## [1243,] -0.08364328 -0.19661738 -1.381143580 -0.590163107
## [1244,] -0.56164758 1.20175764 0.823023833 0.381424656
## [1245,] 5.36560573 3.45131746 0.245489583 0.122334586
## [1246,] -0.08364328 -0.37901412 -0.104210055 1.158694866
## [1247,] -0.08364328 0.04657828 -0.914877397 -1.237888282
## [1248,] -0.08364328 -0.37901412 -0.104210055 1.158694866
## [1249,] 0.58556274 -0.31821520 -0.978459149 0.640514726
## [1250,] 0.77676446 -0.28781575 -0.851295644 0.575742208
## [1251,] 0.77676446 -0.28781575 -0.851295644 0.575742208
## [1252,] 1.06356704 0.41137176 -0.236672039 -0.201528002
## [1253,] -0.94405102 -0.98700326 -0.252567477 0.899604796
## [1254,] -0.94405102 -1.01740271 -0.559879280 0.770059761
## [1255,] 0.39436102 -0.34861466 -0.247268997 0.575742208
## [1256,] 0.96796618 -0.01422063 -1.677858425 1.029149831
## [1257,] 2.59318080 0.41137176 -0.930772835 -1.367433317
## [1258,] -0.37044586 0.68496688 -0.220776601 0.057562068
## [1259,] -0.08364328 -0.40941357 -0.724132140 0.834832278
## [1260,] -0.08364328 -0.40941357 -0.724132140 0.834832278
## [1261,] 0.29876016 0.28977394 -0.226075080 -1.885613457
## [1262,] 0.10755844 -0.68300869 -1.259278555 1.806420041
## [1263,] 1.73277306 0.59376850 0.271981980 -1.043570729
## [1264,] -0.27484500 -0.62220977 0.558099865 0.381424656
## [1265,] 0.96796618 0.13777665 -1.158607447 0.446197173
## [1266,] 0.01195758 -0.31821520 -0.586371676 0.446197173
## [1267,] 0.01195758 -0.31821520 -0.586371676 0.446197173
## [1268,] -0.27484500 -0.83500597 -0.395626419 -1.367433317
## [1269,] 0.48996188 0.13777665 -0.035329823 1.029149831
## [1270,] 1.15916790 1.23215710 -3.150835687 1.223467383
## [1271,] 0.96796618 0.41137176 -3.129641769 2.518917733
## [1272,] 0.77676446 0.53296959 -1.720246259 0.446197173
## [1273,] 0.87236532 -0.07501955 -1.534799482 1.223467383
## [1274,] 1.73277306 -0.07501955 -0.957265232 -1.367433317
## [1275,] 0.10755844 -0.31821520 -1.243383117 -0.460618072
## [1276,] -0.27484500 1.04976036 0.070639764 -0.460618072
## [1277,] -1.23085360 -1.10860108 -0.575774718 -0.201528002
## [1278,] -1.23085360 -1.10860108 -0.204881162 0.251879621
## [1279,] -0.27484500 1.04976036 0.070639764 -0.460618072
## [1280,] -1.23085360 -1.13900054 -1.031443943 -1.108343247
## [1281,] 1.15916790 0.22897502 -0.607565594 0.381424656
## [1282,] 1.15916790 0.22897502 -0.607565594 0.381424656
## [1283,] -0.65724844 -0.74380760 -0.294955312 0.122334586
## [1284,] 0.10755844 0.68496688 0.314369815 -0.331073037
## [1285,] 0.20315930 -0.31821520 -1.052637860 0.510969691
## [1286,] 0.39436102 0.01617882 0.314369815 -1.043570729
## [1287,] 0.10755844 -0.47021249 -1.836812805 0.251879621
## [1288,] -1.23085360 -1.19979945 -2.059348939 -0.590163107
## [1289,] 0.39436102 1.93134461 1.268096100 -0.071982967
## [1290,] 0.39436102 1.93134461 1.268096100 -0.071982967
## [1291,] -0.94405102 -1.04780217 -0.814206289 0.316652138
## [1292,] 0.87236532 0.41137176 -0.194284204 -0.136755484
## [1293,] -0.17924414 -0.56141086 -1.656664507 2.000737593
## [1294,] -0.94405102 -1.04780217 -0.014135906 0.187107103
## [1295,] 0.87236532 0.41137176 -0.194284204 -0.136755484
## [1296,] 3.35798767 0.94336226 -0.618162553 -0.719708142
## [1297,] 3.35798767 0.94336226 -0.618162553 -0.719708142
## [1298,] -0.08364328 -0.53101140 -1.863305202 0.251879621
## [1299,] -0.46604672 -0.86540543 -2.562704478 0.899604796
## [1300,] -1.03965188 -1.13900054 -1.052637860 1.223467383
## [1301,] -0.08364328 -0.56141086 -1.222189200 3.037097873
## [1302,] 0.39436102 0.32017339 -0.406223378 1.871192558
## [1303,] -1.13525274 -0.95660380 -0.522789924 -0.071982967
## [1304,] 1.15916790 0.65456742 -0.920175876 0.316652138
## [1305,] 0.48996188 0.65456742 -0.660550387 -1.237888282
## [1306,] 0.77676446 1.26255656 -0.332044667 -0.071982967
## [1307,] 0.58556274 1.35375493 0.611084659 -0.136755484
## [1308,] -0.08364328 -0.53101140 -0.798310851 0.640514726
## [1309,] 0.58556274 1.35375493 0.611084659 -0.136755484
## [1310,] 1.06356704 0.50257013 -0.395626419 -0.201528002
## [1311,] 0.77676446 1.26255656 -0.332044667 -0.071982967
## [1312,] -0.37044586 -0.59181032 -2.038155021 0.122334586
## [1313,] -0.17924414 -0.16621792 -0.755923016 0.187107103
## [1314,] 0.39436102 0.56336905 -0.618162553 0.575742208
## [1315,] 0.77676446 0.68496688 -0.628759511 0.575742208
## [1316,] 0.10755844 1.35375493 -0.310850750 -0.331073037
## [1317,] 0.01195758 -0.01422063 -2.207706361 4.526865776
## [1318,] -0.56164758 -0.16621792 -0.194284204 -0.849253177
## [1319,] 0.10755844 1.35375493 -0.310850750 -0.331073037
## [1320,] 0.20315930 0.53296959 -0.120105493 -2.662883667
## [1321,] -0.37044586 1.26255656 0.642875535 -0.331073037
## [1322,] 0.01195758 -0.01422063 -2.207706361 4.526865776
## [1323,] -0.65724844 -0.86540543 -1.497710126 -0.849253177
## [1324,] 0.48996188 0.25937448 -0.951966752 -0.849253177
## [1325,] 0.20315930 -0.37901412 -1.031443943 0.510969691
## [1326,] 0.20315930 -0.37901412 -1.031443943 0.510969691
## [1327,] 0.20315930 -0.37901412 -1.031443943 0.510969691
## [1328,] 0.20315930 -0.37901412 -1.031443943 0.510969691
## [1329,] -0.27484500 -0.25741629 -0.660550387 0.187107103
## [1330,] 0.10755844 1.35375493 -0.310850750 -0.136755484
## [1331,] 0.10755844 1.35375493 -0.310850750 -0.136755484
## [1332,] 1.44597048 0.62416796 -0.035329823 -0.331073037
## [1333,] -0.94405102 -0.65260923 -0.496297527 -1.432205834
## [1334,] -0.37044586 0.04657828 0.452130278 -0.849253177
## [1335,] -1.13525274 -1.07820163 -0.353238585 0.510969691
## [1336,] 0.29876016 -0.19661738 -1.524202523 0.446197173
## [1337,] -0.08364328 -0.62220977 -1.200995282 0.316652138
## [1338,] -0.08364328 -0.62220977 -1.200995282 0.316652138
## [1339,] -0.08364328 -0.62220977 -1.200995282 0.316652138
## [1340,] -0.27484500 -0.47021249 -0.724132140 0.316652138
## [1341,] -0.27484500 -0.47021249 -0.724132140 0.316652138
## [1342,] -0.27484500 -0.47021249 -0.724132140 0.316652138
## [1343,] 0.10755844 -0.47021249 -0.453909692 0.381424656
## [1344,] -0.27484500 -0.47021249 -0.724132140 0.316652138
## [1345,] -0.75284930 -0.80460651 0.939590379 -1.432205834
## [1346,] -0.56164758 -0.56141086 -0.326746188 -0.395845554
## [1347,] -1.03965188 -1.01740271 -1.073831778 1.353012418
## [1348,] -0.84845016 -1.04780217 -0.464506651 0.187107103
## [1349,] -0.84845016 -1.04780217 -0.464506651 0.187107103
## [1350,] 0.48996188 -0.16621792 -0.830101727 0.640514726
## [1351,] 1.54157134 0.80656470 -0.109508534 0.187107103
## [1352,] -0.75284930 -0.01422063 -1.815618888 0.057562068
## [1353,] -0.17924414 0.32017339 0.081236723 0.381424656
## [1354,] -0.17924414 0.32017339 0.081236723 0.381424656
## [1355,] -0.84845016 -0.56141086 -0.565177759 0.251879621
## [1356,] -1.03965188 -0.43981303 -1.116219612 0.316652138
## [1357,] -1.13525274 -0.56141086 -1.116219612 0.316652138
## [1358,] 1.06356704 0.62416796 -0.141299410 1.871192558
## [1359,] 3.45358853 1.56655113 0.324966774 -0.201528002
## [1360,] -0.94405102 -0.56141086 0.155415434 -1.561750869
## [1361,] -0.46604672 -0.25741629 0.128923037 -0.460618072
## [1362,] -0.27484500 0.22897502 0.261385021 0.316652138
## [1363,] -0.94405102 -0.56141086 0.155415434 -1.561750869
## [1364,] -0.46604672 0.50257013 -0.120105493 -0.136755484
## [1365,] -0.56164758 -0.47021249 -0.925474355 0.964377313
## [1366,] -0.56164758 -0.13581846 0.187206310 -0.849253177
## [1367,] -0.56164758 -0.04462009 -0.522789924 -0.460618072
## [1368,] -0.65724844 1.77934733 0.266683501 -0.395845554
## [1369,] 0.01195758 0.83696416 -0.098911575 -0.266300519
## [1370,] -1.13525274 -1.16940000 -1.476516209 0.122334586
## [1371,] -0.37044586 0.59376850 -0.273761394 -2.015158492
## [1372,] -1.03965188 -0.98700326 -1.052637860 -0.654935624
## [1373,] -0.37044586 0.59376850 -0.273761394 -2.015158492
## [1374,] 1.73277306 1.29295601 -0.056523740 -0.460618072
## [1375,] 0.01195758 -0.53101140 -1.079130257 0.057562068
## [1376,] -0.27484500 1.62735004 -0.469805131 -0.331073037
## [1377,] -0.84845016 -0.47021249 -1.137413530 -1.302660799
## [1378,] 0.68116360 0.83696416 -0.766519975 2.583690251
## [1379,] -0.56164758 -0.56141086 -0.347940105 -0.136755484
## [1380,] -0.46604672 -0.34861466 -0.623461032 0.316652138
## [1381,] -0.46604672 -0.34861466 -0.623461032 0.316652138
## [1382,] -1.13525274 -0.65260923 -0.745326057 0.446197173
## [1383,] 0.87236532 1.77934733 -0.326746188 -0.071982967
## [1384,] 0.87236532 1.77934733 -0.326746188 -0.071982967
## [1385,] 0.39436102 1.14095873 -0.432715775 -0.784480659
## [1386,] 1.73277306 1.17135819 -0.035329823 -0.784480659
## [1387,] -0.75284930 -0.71340814 -0.342641626 -0.071982967
## [1388,] -0.75284930 -0.71340814 -0.342641626 -0.071982967
## [1389,] -0.84845016 0.07697774 0.229594145 0.251879621
## [1390,] 1.92397478 1.96174407 -0.798310851 -1.367433317
## [1391,] -0.08364328 -0.40941357 -2.027558062 1.741647523
## [1392,] -1.03965188 -0.40941357 -0.332044667 0.381424656
## [1393,] -1.03965188 -1.04780217 -1.349352704 -0.914025694
## [1394,] 0.29876016 0.38097231 -0.332044667 -0.071982967
## [1395,] 1.06356704 0.80656470 -0.030031344 0.057562068
## [1396,] -1.23085360 -1.04780217 0.372653088 -0.007210449
## [1397,] -1.13525274 -1.07820163 0.372653088 -0.007210449
## [1398,] 0.10755844 1.74894787 -0.480402089 -0.201528002
## [1399,] -0.27484500 -0.56141086 -0.077717658 1.353012418
## [1400,] -0.56164758 -0.68300869 -0.607565594 0.705287243
## [1401,] 1.63717220 2.87372778 -0.289656832 -0.395845554
## [1402,] 1.63717220 2.87372778 -0.289656832 -0.395845554
## [1403,] -0.94405102 -0.68300869 -0.236672039 0.834832278
## [1404,] -1.23085360 -1.01740271 -0.395626419 -0.525390589
## [1405,] -0.37044586 -0.01422063 0.928993421 0.770059761
## [1406,] 0.20315930 -0.37901412 -0.819504768 -0.201528002
## [1407,] -0.75284930 -0.74380760 0.346160691 -0.590163107
## [1408,] 2.30637822 0.22897502 -0.925474355 1.482557453
## [1409,] 1.82837392 0.19857556 -0.925474355 -0.266300519
## [1410,] 2.30637822 0.22897502 -0.925474355 1.482557453
## [1411,] -1.03965188 -0.92620434 -1.667261466 1.223467383
## [1412,] -0.75284930 -0.83500597 -0.236672039 1.612102488
## [1413,] -0.75284930 -0.74380760 0.346160691 -0.590163107
## [1414,] -0.37044586 1.29295601 -0.236672039 -1.756068422
## [1415,] -1.23085360 -0.95660380 1.405856563 -0.719708142
## [1416,] -0.75284930 -0.86540543 -0.077717658 1.612102488
## [1417,] -1.23085360 -0.95660380 1.405856563 -0.719708142
## [1418,] 0.48996188 -0.28781575 -0.819504768 0.251879621
## [1419,] -1.23085360 -0.83500597 -0.925474355 -0.978798212
## [1420,] 1.54157134 2.63053212 -0.607565594 -0.266300519
## [1421,] -1.23085360 -0.83500597 -0.925474355 -0.978798212
## [1422,] 0.77676446 0.35057285 -0.130702451 0.187107103
## [1423,] 2.21077736 0.25937448 -0.607565594 0.510969691
## [1424,] -0.17924414 -0.47021249 0.028251929 1.223467383
## [1425,] -0.75284930 -0.71340814 0.346160691 -0.331073037
## [1426,] -0.75284930 -0.71340814 0.346160691 -0.331073037
## [1427,] 0.68116360 0.41137176 0.187206310 0.640514726
## [1428,] -0.94405102 -0.71340814 -0.554580800 -0.071982967
## [1429,] 1.06356704 0.25937448 -0.289656832 -0.007210449
## [1430,] 2.11517650 0.62416796 -0.395626419 0.122334586
## [1431,] 0.20315930 0.01617882 -0.607565594 0.381424656
## [1432,] -0.27484500 0.83696416 -0.501596007 -0.331073037
## [1433,] -1.23085360 -1.07820163 -1.243383117 1.093922348
## [1434,] -0.46604672 -0.65260923 -0.660550387 0.705287243
## [1435,] 3.74039111 1.47535276 3.678904208 -0.849253177
## [1436,] 3.74039111 1.47535276 3.678904208 -0.849253177
## [1437,] 1.06356704 1.32335547 1.268096100 -1.043570729
## [1438,] -0.08364328 -0.22701683 -0.517491445 1.417784936
## [1439,] -1.23085360 -1.16940000 -0.395626419 0.640514726
## [1440,] 1.44597048 1.38415439 -0.575774718 0.057562068
## [1441,] -0.08364328 -0.56141086 -1.084428736 -0.525390589
## [1442,] 0.29876016 1.56655113 0.203101748 -0.978798212
## [1443,] 0.20315930 -0.50061195 0.197803269 0.575742208
## [1444,] -0.75284930 -0.74380760 -1.868603682 0.446197173
## [1445,] 1.44597048 1.38415439 -0.575774718 0.057562068
## [1446,] 0.29876016 1.56655113 0.203101748 -0.978798212
## [1447,] 0.20315930 -0.50061195 0.197803269 0.575742208
## [1448,] 0.58556274 -0.22701683 0.139519996 0.575742208
## [1449,] 2.30637822 0.22897502 0.044147367 0.575742208
## [1450,] -0.08364328 -0.53101140 -1.073831778 -0.525390589
## [1451,] -0.08364328 -0.56141086 -1.084428736 -0.525390589
## [1452,] -0.75284930 -0.89580489 0.303772856 0.122334586
## [1453,] 2.01957564 0.19857556 -1.593082755 0.251879621
## [1454,] 1.06356704 1.17135819 0.166012393 0.640514726
## [1455,] -0.84845016 -0.71340814 1.586004861 -0.654935624
## [1456,] -0.65724844 -0.89580489 -1.100324174 1.223467383
## [1457,] 2.11517650 1.29295601 -2.324272907 -0.071982967
## [1458,] 1.06356704 1.17135819 0.166012393 0.640514726
## [1459,] -0.75284930 -0.80460651 -0.379730981 0.510969691
## [1460,] -0.84845016 -0.95660380 -1.148010489 0.057562068
## [1461,] 1.73277306 0.53296959 0.261385021 0.640514726
## [1462,] -0.94405102 -1.01740271 -0.056523740 1.806420041
## [1463,] -0.17924414 -0.47021249 -0.687042784 0.316652138
## [1464,] -0.17924414 -0.22701683 -0.808907810 0.187107103
## [1465,] 1.73277306 0.19857556 -0.501596007 0.640514726
## [1466,] 1.73277306 0.19857556 -0.501596007 0.640514726
## [1467,] 1.44597048 0.22897502 0.282578939 -0.071982967
## [1468,] 0.29876016 -0.43981303 -0.141299410 1.353012418
## [1469,] 1.44597048 0.22897502 0.282578939 -0.071982967
## [1470,] 0.39436102 0.07697774 0.160713913 -0.007210449
## [1471,] -0.75284930 -0.68300869 -0.512192965 -2.792428702
## [1472,] -0.75284930 -0.92620434 -1.805021929 0.770059761
## [1473,] 0.68116360 -0.07501955 -0.098911575 0.446197173
## [1474,] -0.17924414 -0.04462009 -1.794424971 0.446197173
## [1475,] 3.07118509 1.08015982 3.005997330 -0.978798212
## [1476,] 0.01195758 1.29295601 -2.610390792 1.482557453
## [1477,] 3.07118509 1.08015982 3.005997330 -0.978798212
## [1478,] 0.01195758 1.29295601 -2.610390792 1.482557453
## [1479,] -1.23085360 -0.98700326 0.706457287 0.575742208
## [1480,] -0.56164758 -0.74380760 0.812426875 0.510969691
## [1481,] -0.94405102 -1.01740271 -0.936071314 2.259827663
## [1482,] -0.56164758 -0.74380760 0.812426875 0.510969691
## [1483,] -1.23085360 -1.13900054 0.234892624 0.122334586
## [1484,] -0.94405102 -1.04780217 -0.702938222 0.251879621
## [1485,] -1.13525274 -1.07820163 -0.438014254 1.417784936
## [1486,] -0.75284930 -0.65260923 -0.363835543 0.381424656
## [1487,] -0.65724844 -0.71340814 -0.681744305 0.446197173
## [1488,] -0.65724844 -0.89580489 -0.946668273 1.353012418
## [1489,] -1.03965188 -1.01740271 -1.349352704 2.648462768
## [1490,] -1.13525274 -1.07820163 -1.200995282 0.834832278
## [1491,] -0.75284930 -0.86540543 -1.752037136 0.510969691
## [1492,] -1.03965188 -1.01740271 -1.349352704 2.648462768
## [1493,] -0.94405102 -0.86540543 -1.730843218 1.676875006
## [1494,] 0.68116360 3.05612452 -0.204881162 -0.331073037
## [1495,] -0.08364328 -0.56141086 -1.142712009 0.705287243
## [1496,] -0.08364328 -0.56141086 -0.967862190 0.251879621
## [1497,] 0.68116360 3.05612452 -0.204881162 -0.331073037
## [1498,] -0.84845016 -0.92620434 -0.883086521 0.899604796
## [1499,] -0.84845016 -1.01740271 -0.490999048 1.417784936
## [1500,] -0.84845016 -0.92620434 -0.883086521 0.899604796
## [1501,] -0.75284930 -0.95660380 -0.883086521 -0.331073037
## [1502,] 0.48996188 0.53296959 -0.173090286 -0.978798212
## [1503,] -0.08364328 0.41137176 -0.194284204 -0.007210449
## [1504,] -0.94405102 -0.98700326 -0.634057991 1.288239901
## [1505,] -1.03965188 -1.04780217 -0.369134023 0.316652138
## [1506,] -0.94405102 -1.04780217 -0.395626419 1.547329971
## [1507,] -0.94405102 -0.98700326 -0.596968635 1.288239901
## [1508,] -1.03965188 -1.04780217 -0.369134023 0.316652138
## [1509,] 0.10755844 -0.65260923 0.738248164 0.446197173
## [1510,] -0.84845016 -0.80460651 -1.402337498 -0.201528002
## [1511,] 0.96796618 0.04657828 -0.072419178 1.029149831
## [1512,] 0.29876016 -0.59181032 0.197803269 0.834832278
## [1513,] -0.46604672 -0.89580489 -0.459208172 1.417784936
## [1514,] 0.10755844 0.56336905 -2.016961104 0.122334586
## [1515,] 0.01195758 0.56336905 0.886605586 1.417784936
## [1516,] 0.01195758 0.56336905 0.886605586 1.417784936
## [1517,] 0.68116360 0.35057285 -0.220776601 0.705287243
## [1518,] 1.25476876 -0.07501955 -0.983757628 0.446197173
## [1519,] -0.84845016 -0.80460651 -0.146597890 0.057562068
## [1520,] -0.17924414 -0.59181032 -0.051225261 0.834832278
## [1521,] 1.25476876 -0.07501955 -0.983757628 0.446197173
## [1522,] -0.65724844 -0.71340814 -0.475103610 0.510969691
## [1523,] 0.68116360 0.35057285 -0.220776601 0.705287243
## [1524,] 1.25476876 0.44177122 -0.766519975 0.187107103
## [1525,] 0.58556274 0.01617882 -0.808907810 0.510969691
## [1526,] 0.20315930 -0.37901412 -0.649953429 0.122334586
## [1527,] 0.20315930 -0.25741629 -0.644654949 -0.071982967
## [1528,] -0.08364328 -0.68300869 -0.872489562 -0.136755484
## [1529,] 0.48996188 0.38097231 0.208400228 -0.007210449
## [1530,] 2.49757994 0.16817611 -0.353238585 0.705287243
## [1531,] -0.65724844 -0.86540543 -1.222189200 0.705287243
## [1532,] -0.27484500 -0.56141086 -0.231373559 1.871192558
## [1533,] 0.20315930 -0.74380760 -0.538685362 -0.654935624
## [1534,] 1.54157134 1.14095873 0.224295666 0.510969691
## [1535,] -0.27484500 -0.53101140 -1.471217730 0.705287243
## [1536,] -0.08364328 -0.34861466 -0.448611213 0.316652138
## [1537,] 0.77676446 -0.04462009 -0.777116933 1.871192558
## [1538,] 0.39436102 -0.47021249 -1.010250025 1.223467383
## [1539,] -0.08364328 -0.62220977 -0.724132140 1.676875006
## [1540,] 1.73277306 0.41137176 0.208400228 0.964377313
## [1541,] -0.46604672 -0.43981303 -0.151896369 1.612102488
## [1542,] 0.29876016 0.07697774 -0.045926782 0.575742208
## [1543,] 1.25476876 -0.25741629 -1.073831778 -0.071982967
## [1544,] -0.17924414 -0.83500597 0.441533319 -0.395845554
## [1545,] -0.37044586 -0.83500597 -0.660550387 -0.914025694
## [1546,] 0.01195758 -0.56141086 -0.761221495 0.316652138
## [1547,] 0.10755844 -0.62220977 -0.528088403 0.316652138
## [1548,] -0.37044586 -0.62220977 -1.953379351 1.547329971
## [1549,] 0.29876016 0.10737719 0.573995303 -1.367433317
## [1550,] 0.10755844 -0.68300869 -1.354651184 -0.460618072
## [1551,] 0.10755844 -0.62220977 0.573995303 0.899604796
## [1552,] 0.20315930 -0.59181032 0.494518113 0.834832278
## [1553,] 1.54157134 0.22897502 -0.469805131 1.288239901
## [1554,] 0.20315930 -0.56141086 0.478622675 0.640514726
## [1555,] -0.08364328 -0.71340814 -0.252567477 1.482557453
## [1556,] -0.08364328 -0.68300869 -0.851295644 0.834832278
## [1557,] -0.37044586 -0.80460651 -0.204881162 1.417784936
## [1558,] -0.08364328 -0.71340814 -0.252567477 1.482557453
## [1559,] 4.79200057 2.08334189 0.595189221 -0.590163107
## [1560,] 1.44597048 2.56973321 -0.279059874 -0.654935624
## [1561,] 1.44597048 2.56973321 -0.279059874 -0.654935624
## [1562,] 1.44597048 2.56973321 -0.279059874 -0.654935624
## [1563,] -0.37044586 -0.80460651 -0.681744305 -0.136755484
## [1564,] -0.37044586 -0.80460651 -0.681744305 -0.136755484
## [1565,] -0.37044586 -0.80460651 -0.681744305 -0.136755484
## [1566,] 0.96796618 -0.13581846 -0.983757628 0.510969691
## [1567,] 0.77676446 0.16817611 -0.957265232 0.187107103
## [1568,] -0.37044586 -0.80460651 -0.681744305 -0.136755484
## [1569,] 0.87236532 -0.13581846 -0.241970518 0.187107103
## [1570,] -0.08364328 -0.37901412 -1.476516209 1.093922348
## [1571,] 0.29876016 -0.34861466 -1.773231053 0.381424656
## [1572,] -0.08364328 -0.65260923 -0.851295644 0.834832278
## [1573,] 1.82837392 1.74894787 -0.226075080 0.122334586
## [1574,] -0.08364328 0.10737719 -1.100324174 1.741647523
## [1575,] 0.68116360 1.38415439 0.012356491 0.510969691
## [1576,] -0.37044586 -0.80460651 -1.063234819 -0.331073037
## [1577,] 0.01195758 -0.53101140 -0.459208172 -0.071982967
## [1578,] -0.27484500 -0.59181032 -0.279059874 1.482557453
## [1579,] -0.27484500 -0.80460651 -0.713535181 0.705287243
## [1580,] 0.77676446 -0.43981303 -1.444725333 1.482557453
## [1581,] -0.65724844 -0.62220977 -1.084428736 0.316652138
## [1582,] 0.77676446 -0.43981303 -1.444725333 1.482557453
## [1583,] -0.27484500 -0.59181032 -1.656664507 1.676875006
## [1584,] 1.54157134 1.56655113 -0.512192965 0.122334586
## [1585,] 0.77676446 -0.37901412 -1.010250025 -0.136755484
## [1586,] 0.58556274 0.04657828 -0.957265232 -0.071982967
## [1587,] 1.73277306 0.41137176 -0.967862190 0.187107103
## [1588,] 0.20315930 -0.56141086 -1.015548505 1.547329971
## [1589,] 1.73277306 1.68814895 -1.381143580 -0.266300519
## [1590,] 1.25476876 0.98896144 0.505115072 -0.136755484
## [1591,] 0.96796618 -0.34861466 -1.910991516 0.057562068
## [1592,] 0.01195758 -0.62220977 -1.444725333 2.324600181
## [1593,] 1.25476876 -0.19661738 -0.533386883 0.705287243
## [1594,] 1.15916790 -0.25741629 -0.125403972 0.705287243
## [1595,] 1.54157134 -0.07501955 -0.978459149 0.899604796
## [1596,] 2.21077736 0.13777665 -0.861892603 1.353012418
## [1597,] 1.25476876 -0.19661738 -0.533386883 0.705287243
## [1598,] 1.54157134 -0.07501955 -0.676445826 1.676875006
## [1599,] 0.20315930 -0.13581846 -0.665848867 0.510969691
## sulphates alcohol
## [1,] -0.57902538 -0.95994580
## [2,] 0.12891007 -0.58459423
## [3,] -0.04807379 -0.58459423
## [4,] -0.46103614 -0.58459423
## [5,] -0.57902538 -0.95994580
## [6,] -0.57902538 -0.95994580
## [7,] -1.16897159 -0.95994580
## [8,] -1.10997697 -0.39691844
## [9,] -0.52003076 -0.86610790
## [10,] 0.83684552 0.07227102
## [11,] -0.69701462 -1.14762158
## [12,] 0.83684552 0.07227102
## [13,] -0.81500386 -0.49075633
## [14,] 5.32043671 -1.24145947
## [15,] 1.30880249 -1.14762158
## [16,] 1.60377559 -1.14762158
## [17,] 0.54187242 0.07227102
## [18,] 3.66858733 -1.05378369
## [19,] -0.93299310 -1.33529737
## [20,] 2.48869491 -1.14762158
## [21,] -0.75600924 -0.95994580
## [22,] -0.04807379 -0.67843212
## [23,] 1.48578635 -0.86610790
## [24,] -0.75600924 -0.95994580
## [25,] -0.16606303 -0.67843212
## [26,] -0.57902538 -1.05378369
## [27,] -0.40204152 -0.86610790
## [28,] 1.48578635 -0.86610790
## [29,] -0.63802000 -0.95994580
## [30,] -0.40204152 -0.58459423
## [31,] -0.69701462 -0.30308055
## [32,] -0.52003076 0.16610891
## [33,] 0.01092083 -0.58459423
## [34,] -0.81500386 -0.95994580
## [35,] -0.63802000 -1.14762158
## [36,] -0.63802000 -0.77227001
## [37,] -0.34304689 0.35378470
## [38,] 0.42388318 -0.67843212
## [39,] -1.05098235 -0.58459423
## [40,] 1.01382939 0.07227102
## [41,] 1.01382939 0.07227102
## [42,] -0.87399848 -1.05378369
## [43,] 1.42679173 0.07227102
## [44,] 3.19663036 -0.11540476
## [45,] -0.81500386 -0.86610790
## [46,] -0.57902538 2.51205623
## [47,] 0.42388318 -1.14762158
## [48,] -0.46103614 -0.86610790
## [49,] -0.57902538 -1.14762158
## [50,] -0.46103614 -1.14762158
## [51,] 0.48287780 -1.14762158
## [52,] -0.16606303 -0.95994580
## [53,] -0.10706841 -0.95994580
## [54,] -0.57902538 -0.95994580
## [55,] -0.16606303 -0.20924266
## [56,] -0.75600924 -0.86610790
## [57,] -0.16606303 -0.77227001
## [58,] 0.65986166 -0.95994580
## [59,] -0.40204152 -0.39691844
## [60,] -0.69701462 -0.95994580
## [61,] -0.10706841 -1.14762158
## [62,] 0.30589393 -1.05378369
## [63,] -0.22505765 -0.86610790
## [64,] -0.52003076 -0.58459423
## [65,] -1.58193393 0.44762259
## [66,] -1.58193393 0.44762259
## [67,] -0.46103614 -0.77227001
## [68,] -0.46103614 0.25994681
## [69,] 0.77785090 0.25994681
## [70,] 1.72176484 0.07227102
## [71,] -0.69701462 -0.86610790
## [72,] -1.05098235 -0.86610790
## [73,] -1.05098235 -0.86610790
## [74,] -0.75600924 -1.14762158
## [75,] 0.95483476 -0.77227001
## [76,] 0.01092083 0.07227102
## [77,] 0.01092083 0.07227102
## [78,] -0.63802000 0.25994681
## [79,] -0.81500386 -0.30308055
## [80,] 2.72467339 -1.24145947
## [81,] -0.57902538 -1.14762158
## [82,] 3.66858733 -0.95994580
## [83,] -0.52003076 -1.24145947
## [84,] 2.84266263 -0.95994580
## [85,] 0.71885628 -0.11540476
## [86,] -0.40204152 -0.30308055
## [87,] 7.62122693 -0.49075633
## [88,] -0.10706841 -0.77227001
## [89,] 3.31461960 -0.86610790
## [90,] -0.75600924 -1.33529737
## [91,] -0.69701462 -0.86610790
## [92,] 7.62122693 -0.49075633
## [93,] 7.79821079 -0.58459423
## [94,] -0.10706841 -0.77227001
## [95,] -1.05098235 0.07227102
## [96,] -0.34304689 2.32438044
## [97,] -0.57902538 0.25994681
## [98,] -0.16606303 -1.14762158
## [99,] -0.57902538 -0.58459423
## [100,] -0.40204152 -1.33529737
## [101,] -0.28405227 -0.20924266
## [102,] -0.57902538 -0.02156687
## [103,] -0.40204152 -1.33529737
## [104,] -0.87399848 -1.14762158
## [105,] -1.05098235 -0.95994580
## [106,] -0.87399848 -1.14762158
## [107,] 3.84557119 -1.05378369
## [108,] 0.77785090 -1.05378369
## [109,] 0.83684552 -0.77227001
## [110,] 0.18790469 -1.05378369
## [111,] 1.60377559 -0.86610790
## [112,] 0.01092083 -0.58459423
## [113,] 0.01092083 -0.58459423
## [114,] 0.06991545 -0.67843212
## [115,] 1.60377559 -0.86610790
## [116,] -0.16606303 0.07227102
## [117,] -0.28405227 -0.39691844
## [118,] -0.93299310 -0.95994580
## [119,] -0.34304689 0.44762259
## [120,] -0.69701462 -1.14762158
## [121,] -0.52003076 -1.33529737
## [122,] -0.34304689 0.44762259
## [123,] -0.81500386 -1.14762158
## [124,] -0.75600924 -0.86610790
## [125,] -1.05098235 -0.86610790
## [126,] 0.24689931 -0.95994580
## [127,] -0.99198772 0.44762259
## [128,] -1.05098235 0.44762259
## [129,] 1.54478097 0.07227102
## [130,] -0.52003076 -0.95994580
## [131,] 0.01092083 -0.95994580
## [132,] -0.16606303 2.41821834
## [133,] -0.16606303 2.41821834
## [134,] -0.46103614 -0.58459423
## [135,] -0.63802000 -0.49075633
## [136,] 0.95483476 -0.77227001
## [137,] 0.65986166 -0.86610790
## [138,] -0.10706841 -1.14762158
## [139,] -0.69701462 -0.86610790
## [140,] -0.69701462 -0.86610790
## [141,] 0.95483476 -0.77227001
## [142,] 0.65986166 -0.86610790
## [143,] 0.77785090 3.35659726
## [144,] -0.46103614 -0.95994580
## [145,] 0.77785090 3.35659726
## [146,] -0.22505765 -0.95994580
## [147,] -0.81500386 -0.39691844
## [148,] 0.83684552 -1.05378369
## [149,] -0.46103614 -0.20924266
## [150,] -0.28405227 0.07227102
## [151,] -0.75600924 -0.11540476
## [152,] 7.91620003 -0.95994580
## [153,] -0.69701462 -0.30308055
## [154,] -0.69701462 -0.30308055
## [155,] 0.36488856 0.07227102
## [156,] 0.30589393 0.07227102
## [157,] 0.36488856 0.07227102
## [158,] 0.30589393 0.07227102
## [159,] -0.93299310 -1.05378369
## [160,] -0.52003076 -1.05378369
## [161,] -0.57902538 -0.77227001
## [162,] 2.48869491 -1.14762158
## [163,] -0.57902538 -0.39691844
## [164,] -0.99198772 -0.95994580
## [165,] -0.99198772 -0.95994580
## [166,] -0.22505765 -0.86610790
## [167,] -0.81500386 -0.20924266
## [168,] -1.05098235 -1.33529737
## [169,] -0.63802000 -0.02156687
## [170,] 5.49742057 -0.86610790
## [171,] -1.93590166 -1.24145947
## [172,] -0.28405227 -1.14762158
## [173,] -0.28405227 -1.14762158
## [174,] -0.52003076 1.01064995
## [175,] -1.10997697 -0.86610790
## [176,] 0.71885628 -0.86610790
## [177,] -1.10997697 -0.86610790
## [178,] -0.28405227 0.07227102
## [179,] -0.57902538 -0.77227001
## [180,] -0.40204152 -0.86610790
## [181,] -0.40204152 -0.86610790
## [182,] 2.13472718 -1.05378369
## [183,] -0.81500386 -1.05378369
## [184,] -0.46103614 -1.05378369
## [185,] -0.46103614 -1.05378369
## [186,] -0.75600924 -0.67843212
## [187,] -0.63802000 -1.14762158
## [188,] -0.99198772 -0.67843212
## [189,] -0.63802000 -0.86610790
## [190,] -0.63802000 -0.86610790
## [191,] -0.22505765 -0.95994580
## [192,] -0.22505765 -0.58459423
## [193,] -0.28405227 -0.86610790
## [194,] -0.63802000 -0.67843212
## [195,] -0.63802000 -0.67843212
## [196,] -0.69701462 -0.95994580
## [197,] -0.40204152 -0.20924266
## [198,] 1.83975408 -0.30308055
## [199,] 1.13181863 2.41821834
## [200,] -1.34595545 0.91681205
## [201,] 0.95483476 -0.11540476
## [202,] 2.19372180 -1.05378369
## [203,] 0.77785090 -0.86610790
## [204,] -0.63802000 -1.14762158
## [205,] -0.57902538 -1.14762158
## [206,] 0.65986166 0.35378470
## [207,] 0.65986166 0.35378470
## [208,] -0.75600924 -1.05378369
## [209,] 0.06991545 -0.95994580
## [210,] 1.30880249 0.07227102
## [211,] 1.19081325 1.85519098
## [212,] -0.57902538 -0.39691844
## [213,] 0.06991545 -0.20924266
## [214,] -1.34595545 -0.30308055
## [215,] 0.30589393 -0.58459423
## [216,] 0.60086704 0.07227102
## [217,] -0.52003076 0.54146048
## [218,] -0.63802000 -1.24145947
## [219,] -0.69701462 -0.67843212
## [220,] -0.34304689 -0.86610790
## [221,] -0.40204152 -0.95994580
## [222,] -0.52003076 -0.95994580
## [223,] -0.34304689 -0.86610790
## [224,] -0.34304689 -0.39691844
## [225,] -0.52003076 -0.02156687
## [226,] -0.46103614 0.07227102
## [227,] 5.61540982 -0.86610790
## [228,] -0.16606303 -0.58459423
## [229,] -0.46103614 0.07227102
## [230,] -0.93299310 0.54146048
## [231,] -0.22505765 1.66751520
## [232,] -0.81500386 -0.49075633
## [233,] 0.24689931 -0.77227001
## [234,] -0.93299310 0.54146048
## [235,] -0.63802000 -1.33529737
## [236,] -0.46103614 -1.33529737
## [237,] -0.46103614 -1.33529737
## [238,] -0.46103614 -1.14762158
## [239,] -0.46103614 -1.33529737
## [240,] -0.63802000 -1.33529737
## [241,] 2.54768953 -1.05378369
## [242,] 0.30589393 0.44762259
## [243,] -0.99198772 -0.58459423
## [244,] 1.07282401 -1.14762158
## [245,] 1.07282401 -1.14762158
## [246,] 1.78075946 -0.49075633
## [247,] -0.81500386 -0.86610790
## [248,] -1.22796621 -1.05378369
## [249,] -1.05098235 -0.58459423
## [250,] 1.78075946 -0.49075633
## [251,] 0.71885628 -0.39691844
## [252,] 0.24689931 -0.49075633
## [253,] -0.75600924 0.07227102
## [254,] -0.40204152 -0.86610790
## [255,] 0.24689931 -0.49075633
## [256,] -0.52003076 -1.05378369
## [257,] -0.22505765 -1.14762158
## [258,] 0.06991545 -1.14762158
## [259,] 3.55059808 -0.95994580
## [260,] 0.83684552 0.07227102
## [261,] -0.04807379 -1.05378369
## [262,] -0.34304689 -0.95994580
## [263,] -0.52003076 -0.39691844
## [264,] 0.06991545 -1.05378369
## [265,] 1.24980787 0.44762259
## [266,] 0.36488856 -0.20924266
## [267,] 0.36488856 -0.58459423
## [268,] 1.19081325 2.23054255
## [269,] 1.48578635 -0.95994580
## [270,] 1.83975408 -0.30308055
## [271,] 0.06991545 0.25994681
## [272,] 1.83975408 -0.30308055
## [273,] 0.71885628 -0.30308055
## [274,] -0.10706841 -0.95994580
## [275,] 0.65986166 -0.95994580
## [276,] 0.06991545 0.25994681
## [277,] 1.48578635 -0.95994580
## [278,] 1.83975408 -0.30308055
## [279,] 0.95483476 2.04286677
## [280,] 0.65986166 0.07227102
## [281,] 0.95483476 -1.05378369
## [282,] 2.48869491 -0.49075633
## [283,] -0.52003076 -1.24145947
## [284,] 0.65986166 0.07227102
## [285,] 0.30589393 -0.58459423
## [286,] 0.30589393 -0.58459423
## [287,] 0.60086704 -0.11540476
## [288,] 0.24689931 -0.11540476
## [289,] 1.19081325 0.16610891
## [290,] 1.72176484 -1.14762158
## [291,] 1.19081325 0.16610891
## [292,] 0.30589393 0.07227102
## [293,] -0.10706841 -0.11540476
## [294,] -0.34304689 -0.30308055
## [295,] 0.89584014 -0.86610790
## [296,] -0.10706841 -0.86610790
## [297,] -0.81500386 -0.49075633
## [298,] 0.42388318 -0.77227001
## [299,] 0.83684552 -0.67843212
## [300,] 0.30589393 -0.77227001
## [301,] -0.40204152 0.25994681
## [302,] -0.10706841 -0.30308055
## [303,] 0.36488856 -0.39691844
## [304,] -0.69701462 -0.86610790
## [305,] -0.81500386 -1.14762158
## [306,] -0.40204152 -1.05378369
## [307,] -0.81500386 -0.95994580
## [308,] -0.22505765 -0.86610790
## [309,] -0.16606303 -0.86610790
## [310,] -0.75600924 -0.86610790
## [311,] -0.40204152 -1.05378369
## [312,] -0.69701462 -0.95994580
## [313,] -0.16606303 -0.86610790
## [314,] -0.75600924 -0.95994580
## [315,] 0.12891007 0.54146048
## [316,] -0.04807379 0.54146048
## [317,] -0.04807379 -0.30308055
## [318,] -0.10706841 -0.02156687
## [319,] 0.30589393 1.01064995
## [320,] -0.10706841 -0.02156687
## [321,] 0.30589393 1.01064995
## [322,] -0.22505765 -0.67843212
## [323,] -0.16606303 -1.05378369
## [324,] 0.24689931 -0.86610790
## [325,] 0.18790469 -1.14762158
## [326,] 0.18790469 -1.14762158
## [327,] 0.48287780 1.01064995
## [328,] 1.01382939 1.01064995
## [329,] 0.06991545 -0.67843212
## [330,] -0.22505765 -0.86610790
## [331,] 0.01092083 1.94902887
## [332,] 0.01092083 1.94902887
## [333,] -0.69701462 -0.95994580
## [334,] -0.81500386 0.54146048
## [335,] 0.48287780 1.19832573
## [336,] 1.07282401 1.66751520
## [337,] 0.24689931 1.94902887
## [338,] -0.10706841 -0.11540476
## [339,] 2.01673794 1.01064995
## [340,] 4.14054429 -0.58459423
## [341,] 3.07864112 -1.14762158
## [342,] 1.24980787 0.82297416
## [343,] 0.54187242 -0.58459423
## [344,] 0.54187242 -0.58459423
## [345,] 0.71885628 0.25994681
## [346,] 0.89584014 -0.49075633
## [347,] 1.36779711 1.76135309
## [348,] 1.60377559 1.47983941
## [349,] 1.54478097 -0.39691844
## [350,] 1.19081325 -0.95994580
## [351,] 1.89874870 -0.49075633
## [352,] 1.01382939 -0.95994580
## [353,] 0.42388318 -1.05378369
## [354,] 0.65986166 2.41821834
## [355,] -0.40204152 1.38600152
## [356,] -0.28405227 2.23054255
## [357,] 1.30880249 0.54146048
## [358,] 0.54187242 1.19832573
## [359,] 1.13181863 -0.02156687
## [360,] 0.12891007 -0.58459423
## [361,] 0.24689931 -0.95994580
## [362,] 1.48578635 -0.49075633
## [363,] -0.57902538 -0.39691844
## [364,] 1.24980787 -0.20924266
## [365,] 0.42388318 -0.39691844
## [366,] 1.60377559 1.29216362
## [367,] 0.42388318 -0.39691844
## [368,] 0.18790469 -1.33529737
## [369,] -0.10706841 -0.95994580
## [370,] 2.78366801 1.47983941
## [371,] 0.71885628 -0.49075633
## [372,] 1.24980787 -1.61681104
## [373,] 2.25271642 0.16610891
## [374,] -0.16606303 -1.14762158
## [375,] 0.89584014 0.35378470
## [376,] 1.01382939 1.29216362
## [377,] 2.66567877 0.54146048
## [378,] 2.78366801 1.47983941
## [379,] 1.95774332 2.69973201
## [380,] 0.24689931 0.35378470
## [381,] 0.83684552 -0.95994580
## [382,] 0.83684552 -0.39691844
## [383,] 0.83684552 -0.95994580
## [384,] 0.83684552 -0.95994580
## [385,] 0.48287780 -1.14762158
## [386,] 0.60086704 -0.67843212
## [387,] 0.48287780 -1.14762158
## [388,] -0.69701462 -0.77227001
## [389,] 0.48287780 -1.14762158
## [390,] 0.24689931 -0.39691844
## [391,] 0.95483476 2.32438044
## [392,] 0.83684552 -0.39691844
## [393,] -0.87399848 -0.86610790
## [394,] -0.69701462 -1.24145947
## [395,] -0.52003076 -0.49075633
## [396,] -0.34304689 2.41821834
## [397,] -0.75600924 -0.49075633
## [398,] -0.04807379 0.54146048
## [399,] -0.04807379 0.54146048
## [400,] -0.63802000 -0.95994580
## [401,] -0.75600924 -0.49075633
## [402,] -1.10997697 0.35378470
## [403,] -0.28405227 0.07227102
## [404,] -0.16606303 0.07227102
## [405,] -0.28405227 -1.24145947
## [406,] 0.60086704 -0.30308055
## [407,] -0.04807379 0.35378470
## [408,] -0.16606303 0.35378470
## [409,] 0.24689931 0.82297416
## [410,] -0.52003076 -0.77227001
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## [1253,] -0.46103614 -0.58459423
## [1254,] -0.46103614 -0.86610790
## [1255,] 0.18790469 0.44762259
## [1256,] -1.22796621 0.54146048
## [1257,] -1.40495007 -1.14762158
## [1258,] 0.24689931 0.54146048
## [1259,] -0.16606303 0.82297416
## [1260,] -0.16606303 0.82297416
## [1261,] 2.90165725 -1.05378369
## [1262,] -0.63802000 0.72913627
## [1263,] -0.46103614 -0.58459423
## [1264,] -1.16897159 -0.77227001
## [1265,] 0.36488856 1.29216362
## [1266,] -0.34304689 -0.11540476
## [1267,] -0.34304689 -0.11540476
## [1268,] 1.24980787 0.91681205
## [1269,] -0.63802000 -0.86610790
## [1270,] 0.95483476 3.35659726
## [1271,] 0.54187242 3.35659726
## [1272,] -0.52003076 1.19832573
## [1273,] -0.75600924 0.72913627
## [1274,] -1.34595545 -1.05378369
## [1275,] -0.75600924 0.72913627
## [1276,] -0.69701462 -0.86610790
## [1277,] -0.57902538 1.47983941
## [1278,] -0.34304689 -0.67843212
## [1279,] -0.69701462 -0.86610790
## [1280,] -0.52003076 1.01064995
## [1281,] -0.10706841 -0.02156687
## [1282,] -0.10706841 -0.02156687
## [1283,] 0.12891007 0.44762259
## [1284,] -0.16606303 -0.20924266
## [1285,] -0.57902538 0.44762259
## [1286,] -0.52003076 0.07227102
## [1287,] -0.69701462 1.85519098
## [1288,] -1.69992318 2.41821834
## [1289,] 3.01964650 -0.20924266
## [1290,] 3.01964650 -0.20924266
## [1291,] -0.40204152 0.54146048
## [1292,] 0.54187242 0.44762259
## [1293,] 0.06991545 1.85519098
## [1294,] -0.99198772 -0.67843212
## [1295,] 0.54187242 0.44762259
## [1296,] -1.22796621 -0.86610790
## [1297,] -1.22796621 -0.86610790
## [1298,] -0.28405227 1.57367730
## [1299,] -0.57902538 1.66751520
## [1300,] -1.52293931 0.44762259
## [1301,] -0.28405227 1.94902887
## [1302,] 0.48287780 1.19832573
## [1303,] 0.83684552 0.72913627
## [1304,] 0.01092083 0.72913627
## [1305,] -1.28696083 -1.14762158
## [1306,] -0.75600924 -0.58459423
## [1307,] -0.22505765 -0.30308055
## [1308,] -0.81500386 0.63529838
## [1309,] -0.22505765 -0.30308055
## [1310,] -0.28405227 -1.14762158
## [1311,] -0.75600924 -0.58459423
## [1312,] -0.40204152 2.23054255
## [1313,] -1.10997697 0.07227102
## [1314,] -0.69701462 -0.30308055
## [1315,] -0.75600924 -0.30308055
## [1316,] -0.46103614 -0.58459423
## [1317,] -0.40204152 1.94902887
## [1318,] 0.18790469 1.38600152
## [1319,] -0.46103614 -0.58459423
## [1320,] 3.96356043 -1.24145947
## [1321,] 0.01092083 -0.30308055
## [1322,] -0.40204152 1.94902887
## [1323,] -0.63802000 0.91681205
## [1324,] 0.95483476 0.54146048
## [1325,] -0.34304689 0.16610891
## [1326,] -0.34304689 0.16610891
## [1327,] -0.34304689 0.16610891
## [1328,] -0.34304689 0.16610891
## [1329,] -0.81500386 -1.05378369
## [1330,] -0.57902538 -0.58459423
## [1331,] -0.57902538 -0.58459423
## [1332,] -1.16897159 -1.14762158
## [1333,] -1.34595545 -0.67843212
## [1334,] -0.87399848 -0.77227001
## [1335,] -0.81500386 -0.39691844
## [1336,] 0.01092083 2.04286677
## [1337,] -1.22796621 -0.86610790
## [1338,] -1.22796621 -0.86610790
## [1339,] -1.22796621 -0.86610790
## [1340,] -0.69701462 0.07227102
## [1341,] -0.69701462 0.07227102
## [1342,] -0.69701462 0.07227102
## [1343,] -0.87399848 -0.02156687
## [1344,] -0.69701462 0.07227102
## [1345,] -0.75600924 0.54146048
## [1346,] -0.75600924 -0.20924266
## [1347,] -0.57902538 0.91681205
## [1348,] -1.58193393 -0.86610790
## [1349,] -1.58193393 -0.86610790
## [1350,] -0.81500386 0.35378470
## [1351,] -0.52003076 -0.39691844
## [1352,] -0.87399848 1.29216362
## [1353,] -1.16897159 -0.11540476
## [1354,] -1.16897159 -0.11540476
## [1355,] -0.81500386 -0.39691844
## [1356,] -1.28696083 -0.30308055
## [1357,] -1.28696083 -0.30308055
## [1358,] -0.40204152 0.63529838
## [1359,] -0.93299310 -0.86610790
## [1360,] -0.04807379 -0.36563914
## [1361,] -0.57902538 0.44762259
## [1362,] -0.69701462 -0.30308055
## [1363,] -0.04807379 -0.36563914
## [1364,] -1.05098235 -0.58459423
## [1365,] -0.75600924 1.29216362
## [1366,] -0.22505765 -0.86610790
## [1367,] -0.93299310 -0.58459423
## [1368,] 3.07864112 0.07227102
## [1369,] -0.93299310 -0.58459423
## [1370,] -1.69992318 -0.02156687
## [1371,] 3.01964650 -1.14762158
## [1372,] 2.19372180 1.10448784
## [1373,] 3.01964650 -1.14762158
## [1374,] -1.22796621 -1.05378369
## [1375,] -0.87399848 -0.58459423
## [1376,] -0.81500386 -0.49075633
## [1377,] -1.16897159 -0.39691844
## [1378,] -0.22505765 1.66751520
## [1379,] -0.87399848 -0.49075633
## [1380,] -0.22505765 0.35378470
## [1381,] -0.22505765 0.35378470
## [1382,] -1.22796621 -0.77227001
## [1383,] -0.99198772 -0.49075633
## [1384,] -0.99198772 -0.49075633
## [1385,] -0.93299310 -0.86610790
## [1386,] -1.40495007 -1.14762158
## [1387,] -0.46103614 -0.77227001
## [1388,] -0.46103614 -0.77227001
## [1389,] 0.12891007 -0.11540476
## [1390,] -0.75600924 -0.67843212
## [1391,] -0.40204152 1.94902887
## [1392,] -0.46103614 0.54146048
## [1393,] -1.05098235 -0.58459423
## [1394,] -1.05098235 -0.20924266
## [1395,] -1.05098235 -1.14762158
## [1396,] -0.04807379 -0.81918896
## [1397,] -0.04807379 -0.81918896
## [1398,] -0.81500386 -0.49075633
## [1399,] -0.22505765 -0.20924266
## [1400,] 0.36488856 0.63529838
## [1401,] -0.87399848 -0.49075633
## [1402,] -0.87399848 -0.49075633
## [1403,] 0.95483476 1.38600152
## [1404,] 2.60668415 -0.39691844
## [1405,] -0.22505765 0.25994681
## [1406,] 1.42679173 0.82297416
## [1407,] 1.66277022 0.44762259
## [1408,] 1.60377559 0.25994681
## [1409,] 2.07573256 1.85519098
## [1410,] 1.60377559 0.25994681
## [1411,] -1.28696083 1.38600152
## [1412,] 0.42388318 0.16610891
## [1413,] 1.66277022 0.44762259
## [1414,] 1.42679173 -0.30308055
## [1415,] 0.71885628 -0.77227001
## [1416,] 1.07282401 -0.95994580
## [1417,] 0.71885628 -0.77227001
## [1418,] 0.65986166 1.57367730
## [1419,] -1.16897159 -0.58459423
## [1420,] -1.22796621 -0.49075633
## [1421,] -1.16897159 -0.58459423
## [1422,] -0.46103614 -0.95994580
## [1423,] 1.07282401 0.91681205
## [1424,] 0.06991545 0.54146048
## [1425,] 0.24689931 -0.77227001
## [1426,] 0.24689931 -0.77227001
## [1427,] 0.30589393 1.57367730
## [1428,] -0.46103614 0.72913627
## [1429,] -0.16606303 0.54146048
## [1430,] 1.60377559 0.82297416
## [1431,] -0.22505765 -0.02156687
## [1432,] -0.69701462 -0.49075633
## [1433,] 1.13181863 1.19832573
## [1434,] 0.48287780 -0.30308055
## [1435,] 0.65986166 -1.33529737
## [1436,] 0.65986166 -1.33529737
## [1437,] -0.04807379 -1.80448683
## [1438,] -0.69701462 0.63529838
## [1439,] -1.10997697 -0.11540476
## [1440,] 0.12891007 0.60401908
## [1441,] 0.42388318 0.82297416
## [1442,] -0.81500386 -0.80354931
## [1443,] 0.54187242 -0.58459423
## [1444,] -0.99198772 1.19832573
## [1445,] 0.12891007 0.63529838
## [1446,] -0.81500386 -0.77227001
## [1447,] 0.54187242 -0.58459423
## [1448,] 0.48287780 -0.67843212
## [1449,] 0.42388318 -0.67843212
## [1450,] 0.60086704 0.82297416
## [1451,] 0.42388318 0.82297416
## [1452,] 0.71885628 0.54146048
## [1453,] 0.60086704 1.10448784
## [1454,] -0.46103614 -1.33529737
## [1455,] 0.18790469 0.44762259
## [1456,] -0.16606303 0.44762259
## [1457,] -0.93299310 0.11918997
## [1458,] -0.46103614 -1.33529737
## [1459,] -0.34304689 0.07227102
## [1460,] 0.83684552 1.38600152
## [1461,] 0.12891007 -0.02156687
## [1462,] -0.28405227 -0.39691844
## [1463,] -0.46103614 -0.02156687
## [1464,] -0.75600924 0.35378470
## [1465,] 0.06991545 -0.67843212
## [1466,] 0.06991545 -0.67843212
## [1467,] -0.04807379 -0.39691844
## [1468,] -0.52003076 0.54146048
## [1469,] -0.04807379 -0.39691844
## [1470,] -0.63802000 -0.67843212
## [1471,] -1.10997697 -0.67843212
## [1472,] -0.81500386 2.04286677
## [1473,] 0.77785090 0.63529838
## [1474,] -0.69701462 0.54146048
## [1475,] 0.54187242 -1.52297315
## [1476,] 1.30880249 2.94996639
## [1477,] 0.54187242 -1.52297315
## [1478,] 1.30880249 2.98124569
## [1479,] -0.81500386 -0.20924266
## [1480,] 0.12891007 0.16610891
## [1481,] -0.22505765 -0.30308055
## [1482,] 0.12891007 0.16610891
## [1483,] -0.52003076 -0.11540476
## [1484,] -0.28405227 0.25994681
## [1485,] -0.10706841 0.44762259
## [1486,] -0.63802000 -0.67843212
## [1487,] -0.34304689 -0.11540476
## [1488,] 0.71885628 0.16610891
## [1489,] -0.46103614 0.91681205
## [1490,] 0.60086704 0.35378470
## [1491,] -0.57902538 1.85519098
## [1492,] -0.46103614 0.91681205
## [1493,] -0.69701462 1.43292046
## [1494,] -0.40204152 -0.67843212
## [1495,] 0.24689931 -0.39691844
## [1496,] 0.89584014 0.16610891
## [1497,] -0.40204152 -0.67843212
## [1498,] -0.16606303 1.01064995
## [1499,] -0.46103614 0.35378470
## [1500,] -0.16606303 1.01064995
## [1501,] -0.75600924 -0.77227001
## [1502,] -0.75600924 -0.95994580
## [1503,] -0.69701462 -0.58459423
## [1504,] 0.18790469 0.54146048
## [1505,] -0.63802000 0.91681205
## [1506,] -0.16606303 -0.44383739
## [1507,] 0.01092083 0.35378470
## [1508,] -0.63802000 0.91681205
## [1509,] 0.36488856 0.16610891
## [1510,] 0.24689931 0.63529838
## [1511,] 0.65986166 -0.67843212
## [1512,] 0.06991545 -0.58459423
## [1513,] -0.04807379 -0.02156687
## [1514,] -0.34304689 0.07227102
## [1515,] 0.36488856 -1.11634228
## [1516,] 0.36488856 -1.10070263
## [1517,] 1.83975408 0.16610891
## [1518,] 1.01382939 -0.11540476
## [1519,] -0.16606303 0.07227102
## [1520,] -0.46103614 -0.20924266
## [1521,] 1.01382939 -0.11540476
## [1522,] -1.34595545 -1.28837842
## [1523,] 1.83975408 0.16610891
## [1524,] -0.34304689 -0.02156687
## [1525,] 0.71885628 -0.39691844
## [1526,] -0.10706841 -0.67843212
## [1527,] -0.04807379 -0.77227001
## [1528,] 0.01092083 0.35378470
## [1529,] 0.06991545 -0.30308055
## [1530,] -0.34304689 -0.20924266
## [1531,] 0.18790469 0.82297416
## [1532,] 0.01092083 -0.20924266
## [1533,] 0.12891007 -0.49075633
## [1534,] -0.28405227 -1.33529737
## [1535,] -0.22505765 1.19832573
## [1536,] -0.40204152 -0.67843212
## [1537,] 0.12891007 -0.11540476
## [1538,] -0.10706841 -0.20924266
## [1539,] -0.16606303 1.47983941
## [1540,] 0.71885628 -0.49075633
## [1541,] -0.16606303 1.10448784
## [1542,] 0.60086704 0.44762259
## [1543,] -0.57902538 0.30686575
## [1544,] -0.52003076 -0.02156687
## [1545,] 0.89584014 0.72913627
## [1546,] -0.10706841 -0.30308055
## [1547,] -0.28405227 -0.20924266
## [1548,] -0.87399848 1.57367730
## [1549,] -0.46103614 -0.02156687
## [1550,] 0.24689931 0.91681205
## [1551,] -0.75600924 -0.86610790
## [1552,] -0.69701462 -0.95994580
## [1553,] 0.01092083 0.82297416
## [1554,] -0.34304689 -0.95994580
## [1555,] -0.34304689 0.54146048
## [1556,] 0.12891007 0.11918997
## [1557,] -0.57902538 -0.49075633
## [1558,] -0.34304689 0.54146048
## [1559,] -0.57902538 -0.86610790
## [1560,] -0.81500386 -0.49075633
## [1561,] -0.81500386 -0.49075633
## [1562,] -0.81500386 -0.49075633
## [1563,] -0.69701462 -0.30308055
## [1564,] -0.69701462 -0.30308055
## [1565,] -0.69701462 -0.30308055
## [1566,] 0.95483476 0.44762259
## [1567,] 0.30589393 0.72913627
## [1568,] -0.69701462 -0.30308055
## [1569,] -0.40204152 -1.14762158
## [1570,] -0.52003076 1.01064995
## [1571,] 1.60377559 1.85519098
## [1572,] -0.04807379 0.63529838
## [1573,] -0.87399848 -0.86610790
## [1574,] 0.06991545 1.94902887
## [1575,] -1.05098235 0.07227102
## [1576,] -0.10706841 1.29216362
## [1577,] 0.71885628 0.35378470
## [1578,] -0.34304689 1.38600152
## [1579,] 0.06991545 0.82297416
## [1580,] -0.34304689 0.82297416
## [1581,] -0.34304689 1.38600152
## [1582,] -0.34304689 0.82297416
## [1583,] -0.93299310 1.38600152
## [1584,] -0.22505765 -0.58459423
## [1585,] 0.83684552 1.10448784
## [1586,] 1.07282401 1.01064995
## [1587,] 1.13181863 0.91681205
## [1588,] 0.01092083 0.44762259
## [1589,] 0.71885628 2.23054255
## [1590,] -0.69701462 -1.14762158
## [1591,] 0.95483476 1.10448784
## [1592,] -0.57902538 1.10448784
## [1593,] 0.54187242 0.54146048
## [1594,] 0.95483476 -0.86610790
## [1595,] -0.46103614 0.07227102
## [1596,] 0.60086704 0.72913627
## [1597,] 0.54187242 0.54146048
## [1598,] 0.30589393 -0.20924266
## [1599,] 0.01092083 0.54146048
## attr(,"scaled:center")
## fixed acidity volatile acidity citric acid
## 8.31963727 0.52782051 0.27097561
## residual sugar chlorides free sulfur dioxide
## 2.53880550 0.08746654 15.87492183
## total sulfur dioxide density pH
## 46.46779237 0.99674668 3.31111320
## sulphates alcohol
## 0.65814884 10.42298311
## attr(,"scaled:scale")
## fixed acidity volatile acidity citric acid
## 1.741096318 0.179059704 0.194801137
## residual sugar chlorides free sulfur dioxide
## 1.409928060 0.047065302 10.460156970
## total sulfur dioxide density pH
## 32.895324478 0.001887334 0.154386465
## sulphates alcohol
## 0.169506980 1.065667582The subset of data represented by matsurv contains standardized values of three chemical properties (fixed acidity, volatile acidity, citric acid) for wines categorized as “good” quality. Each row represents a different wine sample.
matsurv <- matstand[quality == "good",]The variance of alcohol content significantly differs between wines of “good” quality and those of “bad” quality.
This is supported by the calculated F-statistic of approximately 2.1294 and the associated p-value, which is virtually zero.
The confidence interval for the ratio of variances (1.852 to 2.446) further confirms this difference.
var.test(alcohol[quality=="good"],alcohol[quality=="bad"])##
## F test to compare two variances
##
## data: alcohol[quality == "good"] and alcohol[quality == "bad"]
## F = 2.1294, num df = 854, denom df = 743, p-value < 0.00000000000000022
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.852111 2.446455
## sample estimates:
## ratio of variances
## 2.129366matnosurv <- matstand[quality == "bad",]
vecmediansurv <- apply(matsurv, 2, median)Fixed Acidity: The median fixed acidity is slightly below the mean value.
Volatile Acidity: The median volatile acidity is moderately negative, indicating a lower level compared to the mean.
Citric Acid: The median citric acid content is positive, suggesting a higher concentration compared to the mean.
Residual Sugar: The median residual sugar content is negative, indicating a lower level compared to the mean.
Chlorides: The median chloride content is negative, suggesting a lower concentration compared to the mean.
Free Sulfur Dioxide: The median free sulfur dioxide level is negative, indicating a lower concentration compared to the mean.
Total Sulfur Dioxide: The median total sulfur dioxide level is moderately negative, suggesting a lower concentration compared to the mean.
Density: The median density is negative, indicating a lower value compared to the mean.
pH: The median pH value is close to zero, suggesting it is around the mean level.
Sulphates: The median sulphates content is positive, indicating a higher concentration compared to the mean.
Alcohol: The median alcohol content is positive, suggesting a higher concentration compared to the mean.
vecmediannosurv <- apply(matnosurv, 2, median)
matabsdevsurv <- abs(matsurv - matrix(rep(vecmediansurv,nrow(matsurv)),nrow=nrow(matsurv), byrow=TRUE))
matabsdevnosurv <- abs(matnosurv - matrix(rep(vecmediannosurv,nrow(matnosurv)),nrow=nrow(matnosurv), byrow=TRUE))matabsdev.all <- rbind(matabsdevsurv,matabsdevnosurv)
matabsdev.all <- data.frame(quality, matabsdev.all)
t.test(matabsdev.all$alcohol[quality == "good"],matabsdev.all$alcohol[quality == "bad"], alternative="less",var.equal = TRUE)##
## Two Sample t-test
##
## data: matabsdev.all$alcohol[quality == "good"] and matabsdev.all$alcohol[quality == "bad"]
## t = -0.35819, df = 1597, p-value = 0.3601
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 0.03840288
## sample estimates:
## mean of x mean of y
## 0.6874677 0.6981506The calculated t-value of approximately -0.35819 suggests that the difference in means of the absolute deviations of alcohol content between wines categorized as “good” quality and “bad” quality is relatively small.
The alternative hypothesis states that the true difference in means is less than 0.
Based on the results of the one-sided t-test, with a significance level of 0.05, there is insufficient evidence to reject the null hypothesis. Therefore, we cannot conclude that the absolute deviations of alcohol content between wines categorized as “good” quality and “bad” quality are significantly different.
t.test(matabsdev.all$fixed.acidity[quality == "good"],matabsdev.all$fixed.acidity[quality == "bad"], alternative="less",var.equal = TRUE)##
## Two Sample t-test
##
## data: matabsdev.all$fixed.acidity[quality == "good"] and matabsdev.all$fixed.acidity[quality == "bad"]
## t = 0.2995, df = 1597, p-value = 0.6177
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 0.06746675
## sample estimates:
## mean of x mean of y
## 0.7598224 0.7494351d.all <- data.frame(quality,sqrt(rowSums(matabsdev.all[,-1]^2)))
d.all## quality sqrt.rowSums.matabsdev.all....1..2..
## 1 bad 3.1094172
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## 3 bad 2.2315687
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## 6 bad 2.9234690
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## 11 bad 3.6480682
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## 1445 good 4.5889211
## 1446 good 2.4943586
## 1447 bad 2.0316370
## 1448 bad 4.3838824
## 1449 bad 4.3838824
## 1450 good 6.3603590
## 1451 good 3.1616156
## 1452 good 2.2839315
## 1453 good 1.7514684
## 1454 bad 2.2020084
## 1455 good 2.4250709
## 1456 good 2.2020084
## 1457 good 1.8342311
## 1458 bad 1.7514684
## 1459 bad 3.5622081
## 1460 good 2.0949178
## 1461 good 3.2587614
## 1462 bad 1.4404966
## 1463 good 2.8307977
## 1464 good 1.6383393
## 1465 bad 3.0345711
## 1466 bad 2.4376061
## 1467 good 2.4376061
## 1468 bad 2.4376061
## 1469 good 3.5361234
## 1470 bad 3.1425417
## 1471 bad 2.0948558
## 1472 bad 2.0948558
## 1473 good 2.1619062
## 1474 bad 2.1312936
## 1475 bad 1.4893102
## 1476 good 1.4893102
## 1477 bad 1.5856211
## 1478 good 2.5779056
## 1479 bad 2.5687649
## 1480 bad 4.9904430
## 1481 bad 1.7299530
## 1482 bad 1.6974587
## 1483 bad 1.6431050
## 1484 bad 1.4771540
## 1485 bad 1.5728124
## 1486 bad 2.9420983
## 1487 bad 8.4289104
## 1488 bad 8.4289104
## 1489 bad 2.9898286
## 1490 good 4.5991070
## 1491 good 1.8987818
## 1492 bad 2.7439073
## 1493 bad 2.3398103
## 1494 bad 2.2266092
## 1495 good 2.2266092
## 1496 good 2.0990092
## 1497 bad 3.0249080
## 1498 good 1.3972022
## 1499 good 1.3972022
## 1500 good 3.1034169
## 1501 bad 3.6088633
## 1502 bad 1.6990020
## 1503 bad 2.5203648
## 1504 good 1.1757489
## 1505 good 2.2791295
## 1506 bad 1.8266720
## 1507 good 1.9948022
## 1508 good 3.5563872
## 1509 good 3.5563872
## 1510 bad 3.2329625
## 1511 good 3.6107036
## 1512 bad 2.9168627
## 1513 good 3.6107036
## 1514 good 2.3871395
## 1515 good 3.3763363
## 1516 good 2.3871395
## 1517 bad 1.6189337
## 1518 good 2.6315758
## 1519 bad 2.1994920
## 1520 bad 2.1906761
## 1521 good 4.3650671
## 1522 bad 1.5951100
## 1523 bad 3.8232424
## 1524 bad 3.3829256
## 1525 good 2.4012711
## 1526 bad 1.7716557
## 1527 good 3.0534690
## 1528 good 1.7716557
## 1529 good 1.9238624
## 1530 good 2.9761321
## 1531 good 2.1424128
## 1532 bad 2.1424128
## 1533 good 2.4446596
## 1534 bad 2.9095561
## 1535 good 2.3998841
## 1536 good 2.3998841
## 1537 good 3.3331056
## 1538 good 2.4803986
## 1539 bad 3.4413687
## 1540 bad 3.9318559
## 1541 good 2.6336330
## 1542 good 10.0669714
## 1543 good 10.0669714
## 1544 good 3.5590159
## 1545 good 3.1446521
## 1546 good 3.2807323
## 1547 bad 3.1446521
## 1548 bad 2.4445974
## 1549 bad 2.7497861
## 1550 good 3.1800703
## 1551 bad 1.2493561
## 1552 bad 1.5709388
## 1553 good 2.8388148
## 1554 bad 4.0620725
## 1555 good 4.0620725
## 1556 good 3.7690529
## 1557 bad 3.2792845
## 1558 good 3.2792845
## 1559 bad 2.0092709
## 1560 bad 2.4424194
## 1561 bad 0.7194663
## 1562 bad 2.6142938
## 1563 bad 3.4481979
## 1564 bad 1.7163719
## 1565 bad 3.2885724
## 1566 good 2.0621075
## 1567 good 1.7271216
## 1568 bad 1.7693906
## 1569 bad 3.2885724
## 1570 good 2.0709884
## 1571 good 1.5462119
## 1572 good 2.5067011
## 1573 bad 2.5710746
## 1574 good 3.1648003
## 1575 good 2.8412654
## 1576 good 1.5813949
## 1577 good 3.7473054
## 1578 good 3.1511710
## 1579 good 1.8155693
## 1580 bad 1.7575474
## 1581 good 1.7560371
## 1582 bad 2.6153651
## 1583 bad 7.1697155
## 1584 bad 3.2039983
## 1585 good 3.2039983
## 1586 good 3.2039983
## 1587 good 1.4648394
## 1588 good 1.4648394
## 1589 good 1.4648394
## 1590 bad 1.4648394
## 1591 good 1.4605947
## 1592 good 2.8808040
## 1593 good 3.1210227
## 1594 good 3.1210227
## 1595 bad 3.6012498
## 1596 good 2.7460387
## 1597 good 4.5210542
## 1598 bad 2.6327556
## 1599 good 2.9655064with(d.all, t.test(dij[quality=="good"], dij[quality=="bad"],var.equal=TRUE, alternative="less"))##
## Two Sample t-test
##
## data: dij[quality == "good"] and dij[quality == "bad"]
## t = 1.3082, df = 1597, p-value = 0.9045
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 0.2123522
## sample estimates:
## mean of x mean of y
## 3.012486 2.918443The calculated t-value is 1.3082 with a corresponding p-value of 0.9045. This p-value indicates that there is insufficient evidence to reject the null hypothesis, suggesting that the mean “dij” value in the “good” quality group is not significantly different from the mean “dij” value in the “bad” quality group.
Therefore, we do not have enough statistical evidence to conclude that there is a significant difference in the “dij” values between the two quality groups.
sprintf("d-values for Survivors: Mean = %2.3f, Variance = %2.3f",mean(d.all$dij[quality=="good"]),var(d.all$dij[quality=="bad"]))## [1] "d-values for Survivors: Mean = 3.012, Variance = 2.085"******************3) Application of different MVA models (10 points)***************************************************** [1] PCA [2] Cluster Analysis [3] Factor Analysis [4] Multi Regression [5] Logisitic Regression [6] LDA
[1] Data Preprocessing for application of models: dimensionality reduction: [a] PCA Analysis [b] Factor analysis
Models:
[1] Cluster analysis: (Grouping of target variable) [2] Multiple Regression:(Prediction of target variable) [3] Logistic Regression: (Prediction of target variable) [4] Linear Discriminant Analysis (LDA): (Prediction of target variable)
Goal:
[1] Calculate std deviation of Principal components from the mean indicating some behavior of PC’s based on attributes calculated
example: High values shows high variability in Principle components
Observations:
Standard Deviation [1] PC1, PC2, PC3, PC4, PC5: Have the majority of standard deviation (Total)
[2] PC11, PC10 have the lowest standard deviation indicating less meaning
Rotation (n x k): [Matrix] [1] Indicates relation between original attributes and and Principal Components
Observation: (relation crossing 0.30):
[1] PC1: fixed acidity (positive relation),citric acidity (positive relation), pH (negative) indicate highest relativity with PC1 either positive or negative.
[2] PC2: free sulphur dioxide (negative), Total Sulphur dioxide (negative) and alcohol (+ve) relation.
[3] PC3: Volatile acidity (-ve),alcohol has high relation (+ve), free sulphur dioxide (+ve)
Inferences:
alcohol and sulphur dioxide have high values in PC3 indicating +ve correlation between them.
+ve and -ve range in each PC are relation of variables with respective PC’s and their correlation with each other.
PC1, PC2, PC3, PC4 have majority of proportion of variance indicating to be evaluated further.
But cumulative proportion is low for PC1, PC2, PC3 and increasing further indicating ambiguity as it is the proportion of variance explained.
# Using prcomp to compute the principal components (eigenvalues and eigenvectors). With scale=TRUE, variable means are set to zero, and variances set to one
#prcomp: return PCA values of dataframe
#scale: Scaling variables before performing PCA to obtain uniformity in the values
df_pca <- prcomp(df[1:11],scale=TRUE)
summary(df_pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7604 1.3878 1.2452 1.1015 0.97943 0.81216 0.76406
## Proportion of Variance 0.2817 0.1751 0.1410 0.1103 0.08721 0.05996 0.05307
## Cumulative Proportion 0.2817 0.4568 0.5978 0.7081 0.79528 0.85525 0.90832
## PC8 PC9 PC10 PC11
## Standard deviation 0.65035 0.58706 0.42583 0.24405
## Proportion of Variance 0.03845 0.03133 0.01648 0.00541
## Cumulative Proportion 0.94677 0.97810 0.99459 1.00000Checking eigen vectors:
To check composition of each attribute in respective PC’s
Observation:
Eigen values are the center data points in the vector which do not change a lot after rotation.
Calculating Eigen vector values/Standard Deviation information contained by each PC’s Function: df_pca$sdev^2
Squaring to penalize and show the impact of each std deviation
[1] PC1: contains the highest standard deviation: 3.09913244
[2] PC2, PC3, PC4, PC5: 1.92590969 1.55054349 1.21323253 0.95929207 Following principal components contain high amount of standard deviation information.
Summation of eigen_df calculated with standard deviation and eigen values always amount to the number of Principal components present
# loading (eigenvectors) are stored in df_pca$rotation
print(df_pca$rotation)## PC1 PC2 PC3 PC4
## fixed acidity 0.48931422 0.110502738 -0.12330157 0.229617370
## volatile acidity -0.23858436 -0.274930480 -0.44996253 -0.078959783
## citric acid 0.46363166 0.151791356 0.23824707 0.079418256
## residual sugar 0.14610715 -0.272080238 0.10128338 0.372792562
## chlorides 0.21224658 -0.148051555 -0.09261383 -0.666194756
## free sulfur dioxide -0.03615752 -0.513566812 0.42879287 0.043537818
## total sulfur dioxide 0.02357485 -0.569486959 0.32241450 0.034577115
## density 0.39535301 -0.233575490 -0.33887135 0.174499758
## pH -0.43851962 -0.006710793 0.05769735 0.003787746
## sulphates 0.24292133 0.037553916 0.27978615 -0.550872362
## alcohol -0.11323206 0.386180959 0.47167322 0.122181088
## PC5 PC6 PC7 PC8
## fixed acidity -0.08261366 -0.10147858 0.35022736 -0.17759545
## volatile acidity 0.21873452 -0.41144893 0.53373510 -0.07877531
## citric acid -0.05857268 -0.06959338 -0.10549701 -0.37751558
## residual sugar 0.73214429 -0.04915555 -0.29066341 0.29984469
## chlorides 0.24650090 -0.30433857 -0.37041337 -0.35700936
## free sulfur dioxide -0.15915198 0.01400021 0.11659611 -0.20478050
## total sulfur dioxide -0.22246456 -0.13630755 0.09366237 0.01903597
## density 0.15707671 0.39115230 0.17048116 -0.23922267
## pH 0.26752977 0.52211645 0.02513762 -0.56139075
## sulphates 0.22596222 0.38126343 0.44746911 0.37460432
## alcohol 0.35068141 -0.36164504 0.32765090 -0.21762556
## PC9 PC10 PC11
## fixed acidity -0.194020908 0.24952314 -0.639691452
## volatile acidity 0.129110301 -0.36592473 -0.002388597
## citric acid 0.381449669 -0.62167708 0.070910304
## residual sugar -0.007522949 -0.09287208 -0.184029964
## chlorides -0.111338666 0.21767112 -0.053065322
## free sulfur dioxide -0.635405218 -0.24848326 0.051420865
## total sulfur dioxide 0.592115893 0.37075027 -0.068701598
## density -0.020718675 0.23999012 0.567331898
## pH 0.167745886 0.01096960 -0.340710903
## sulphates 0.058367062 -0.11232046 -0.069555381
## alcohol -0.037603106 0.30301450 0.314525906# variable standard deviations stored in df_pca$scale
print(df_pca$scale)## fixed acidity volatile acidity citric acid
## 1.741096318 0.179059704 0.194801137
## residual sugar chlorides free sulfur dioxide
## 1.409928060 0.047065302 10.460156970
## total sulfur dioxide density pH
## 32.895324478 0.001887334 0.154386465
## sulphates alcohol
## 0.169506980 1.065667582(eigen_df <- df_pca$sdev^2)## [1] 3.09913244 1.92590969 1.55054349 1.21323253 0.95929207 0.65960826
## [7] 0.58379122 0.42295670 0.34464212 0.18133317 0.05955831names(eigen_df) <- paste("PC",1:11,sep="") #formatting PC with column no. for respective PC's
eigen_df## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## 3.09913244 1.92590969 1.55054349 1.21323253 0.95929207 0.65960826 0.58379122
## PC8 PC9 PC10 PC11
## 0.42295670 0.34464212 0.18133317 0.05955831sumlambdas <- sum(eigen_df)
sumlambdas## [1] 11Scaling/Proportioning the Principal component eigen values by diving by the sum values of eigen values calculated for each PC’s
Even with scaling we can observe that
[1] PC1, PC2, PC3, PC4, PC5 hold the majority percentage of eigen values
starting in order from: 28%, 17%, 14%, 11%, 8%
[2] Rest of the PC’s do not hold high proportion of std deviation indicating less meaning.
propvar <- eigen_df/sumlambdas
propvar## PC1 PC2 PC3 PC4 PC5 PC6
## 0.281739313 0.175082699 0.140958499 0.110293866 0.087208370 0.059964388
## PC7 PC8 PC9 PC10 PC11
## 0.053071929 0.038450609 0.031331102 0.016484833 0.005414392******************4) Model Insights PCA (10 points)************
cumsum(propvar): is used to add the values (Indicating increase in cumulative proportion) of previous Principal components to the next Principal component totalling to 1 as the values are scaled from 0 to 1
Barplot to explain cumulative contribution of each PC’s
Obervation:
[1] PC1 to PC4 have highest contribution [2] After PC5 the contribution is minimal which only amounts to only 0.2 percent of entire values
library(waterfall)## Warning: package 'waterfall' was built under R version 4.3.2cumvar_df <- cumsum(propvar)
cumvar_df## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 0.2817393 0.4568220 0.5977805 0.7080744 0.7952827 0.8552471 0.9083191 0.9467697
## PC9 PC10 PC11
## 0.9781008 0.9945856 1.0000000# Load the waterfall package
library(waterfall)
# Create a data frame for the waterfall chart
waterfall_data <- data.frame(
labels = paste("PC", 1:length(propvar)),
values = propvar
)
# Calculate the cumulative sum of the proportion of variance
cumulative_propvar <- cumsum(waterfall_data$values)
# Plot the waterfall chart with rotated x-axis labels
barplot(cumulative_propvar, names.arg = waterfall_data$labels,
main = "Waterfall Chart of Cumulative Proportion of Variance",
xlab = "Principal Component", ylab = "Cumulative Proportion of Variance",
las = 2, cex.names = 0.8) # las = 2 for vertical labels, cex.names to adjust label size
#Based on the cumulative plot we can select PC1 to PC5 as they indicate high contribution to the total******************4) Model Insights PCA (10 points)************ eigen_df: This seems to be a data frame containing information about eigenvectors or eigenvalues from some analysis.
propvar: This is likely a vector containing the proportion of variance explained by each principal component.
cumvar_df: This could be another data frame containing cumulative proportion of variance explained by each principal component.
rbind(eigen_df,propvar,cumvar_df): to combine above mentioned dataframes
Observation:
matlambdas: (Combined dataframe) [1] PC1 and PC2 have highest proportion of eigen values indicating highest variance captured
[2] Hence proportion of variance is highest for PC1 and PC2
Second Observation is for original PCA vector: (same as above just not combined hence showing lesser values)
[1] PC1 and PC2 have highest proportion of eigen values indicating highest variance captured
[2] Hence proportion of variance is highest for PC1 and PC2
Mapping Dependent variable with PC1 and PC2:
Observations:
[1] Quality=Bad are both negative and positive components of PC1 and PC2 but more towards negative
[2] Quality = Good are capturing both negative and positive value in most of the Principal Components but more towards positive
#Combining mulltiple dataframes of PCA calculation done
matlambdas <- rbind(eigen_df,propvar,cumvar_df)
#Assigning row names to combined data frame
rownames(matlambdas) <- c("Eigenvalues","Prop. variance","Cum. prop. variance")
#rounding values of combined dataframe to 4 decimal points
round(matlambdas,4)## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## Eigenvalues 3.0991 1.9259 1.5505 1.2132 0.9593 0.6596 0.5838 0.4230
## Prop. variance 0.2817 0.1751 0.1410 0.1103 0.0872 0.0600 0.0531 0.0385
## Cum. prop. variance 0.2817 0.4568 0.5978 0.7081 0.7953 0.8552 0.9083 0.9468
## PC9 PC10 PC11
## Eigenvalues 0.3446 0.1813 0.0596
## Prop. variance 0.0313 0.0165 0.0054
## Cum. prop. variance 0.9781 0.9946 1.0000summary(matlambdas)## PC1 PC2 PC3 PC4
## Min. :0.2817 Min. :0.1751 Min. :0.1410 Min. :0.1103
## 1st Qu.:0.2817 1st Qu.:0.3160 1st Qu.:0.3694 1st Qu.:0.4092
## Median :0.2817 Median :0.4568 Median :0.5978 Median :0.7081
## Mean :1.2209 Mean :0.8526 Mean :0.7631 Mean :0.6772
## 3rd Qu.:1.6904 3rd Qu.:1.1914 3rd Qu.:1.0742 3rd Qu.:0.9607
## Max. :3.0991 Max. :1.9259 Max. :1.5505 Max. :1.2132
## PC5 PC6 PC7 PC8
## Min. :0.08721 Min. :0.05996 Min. :0.05307 Min. :0.03845
## 1st Qu.:0.44125 1st Qu.:0.35979 1st Qu.:0.31843 1st Qu.:0.23070
## Median :0.79528 Median :0.65961 Median :0.58379 Median :0.42296
## Mean :0.61393 Mean :0.52494 Mean :0.51506 Mean :0.46939
## 3rd Qu.:0.87729 3rd Qu.:0.75743 3rd Qu.:0.74606 3rd Qu.:0.68486
## Max. :0.95929 Max. :0.85525 Max. :0.90832 Max. :0.94677
## PC9 PC10 PC11
## Min. :0.03133 Min. :0.01648 Min. :0.005414
## 1st Qu.:0.18799 1st Qu.:0.09891 1st Qu.:0.032486
## Median :0.34464 Median :0.18133 Median :0.059558
## Mean :0.45136 Mean :0.39747 Mean :0.354991
## 3rd Qu.:0.66137 3rd Qu.:0.58796 3rd Qu.:0.529779
## Max. :0.97810 Max. :0.99459 Max. :1.000000summary(df_pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7604 1.3878 1.2452 1.1015 0.97943 0.81216 0.76406
## Proportion of Variance 0.2817 0.1751 0.1410 0.1103 0.08721 0.05996 0.05307
## Cumulative Proportion 0.2817 0.4568 0.5978 0.7081 0.79528 0.85525 0.90832
## PC8 PC9 PC10 PC11
## Standard deviation 0.65035 0.58706 0.42583 0.24405
## Proportion of Variance 0.03845 0.03133 0.01648 0.00541
## Cumulative Proportion 0.94677 0.97810 0.99459 1.00000# Identifying the scores by their Quality of wine
dftyp_pca <- cbind(data.frame(quality),df_pca$x)
dftyp_pca## quality PC1 PC2 PC3 PC4
## 1 bad -1.6190233808 -0.4508090596 -1.773899198 -0.04372663171
## 2 bad -0.7989199991 -1.8559724342 -0.911405040 -0.54789456791
## 3 bad -0.7482450102 -0.8817630087 -1.171027882 -0.41089212728
## 4 good 2.3569352844 0.2698915759 0.243412547 0.92815961469
## 5 bad -1.6190233808 -0.4508090596 -1.773899198 -0.04372663171
## 6 bad -1.5832119779 -0.5690168712 -1.537804961 -0.02374245982
## 7 bad -1.1011195532 -0.6078248455 -1.075578309 0.34385163565
## 8 good -2.2480043771 0.4167044143 -0.986528559 0.00120300307
## 9 good -1.0865466057 0.3084729917 -1.517675415 -0.00331386783
## 10 bad 0.6545847432 -1.6646866116 1.209097967 0.82437710673
## 11 bad -1.1248523852 -0.8602430731 -0.771974366 -0.46421412274
## 12 bad 0.6545847432 -1.6646866116 1.209097967 0.82437710673
## 13 bad -3.0259143987 -0.4933853398 -0.220097360 -0.59443976739
## 14 bad 1.5384164486 0.2350522944 0.047491211 -3.79120039139
## 15 bad 1.5647785548 -4.7937473441 1.414634343 -1.36051814429
## 16 bad 1.6207417506 -4.7785748399 1.520532139 -1.40832864333
## 17 good 1.1741982777 -1.1424408135 2.393564315 -0.14535764355
## 18 bad 2.6943618301 -1.2151140630 -0.123356313 -6.37126562650
## 19 bad -0.7222123697 -0.5045140808 -1.746537087 0.63975212106
## 20 good 3.3342696553 -0.7534330494 0.458016739 -5.13922803589
## 21 good 0.6234495719 -0.4399756688 0.981867530 0.62519819215
## 22 bad -0.2059201160 -0.9676464597 0.454821527 0.13454395694
## 23 bad 0.6961895123 0.3968745776 -0.255795789 -1.52335519109
## 24 bad 0.0530890262 -0.4164124715 -0.932138977 0.25437446961
## 25 good -0.8782805799 -0.3810139890 0.071530733 -0.15507514403
## 26 bad -1.1642698338 0.6101641599 -0.567739901 -0.41553904912
## 27 bad -0.2393189176 1.1936373618 -0.998120251 -0.16760156174
## 28 bad 0.6961895123 0.3968745776 -0.255795789 -1.52335519109
## 29 bad -1.7346751593 -0.5886114978 -1.576598362 -0.15473933444
## 30 good -1.3219581716 0.4102782338 -1.538692327 -0.24088136088
## 31 bad -1.5592690050 -1.1615403668 -0.273018636 -0.11645018087
## 32 good -1.8050008848 -0.6707673531 -0.490023764 -0.31304980492
## 33 bad -0.0895459518 -1.5639453488 -0.194064947 -0.14831611622
## 34 good -0.0867786299 -4.3959233762 0.596343282 2.78979899715
## 35 bad -1.0837568763 -0.0621479209 0.044503698 -0.63563271904
## 36 good -0.5614524459 -0.5195262188 -1.940595777 0.92849681434
## 37 good -0.8278540247 0.9882136281 -1.171732413 0.04815887874
## 38 good 0.4339239895 0.5493533317 -0.111773452 -0.11500590920
## 39 bad -2.9857396410 -0.3195738755 -2.429071380 -1.96361525148
## 40 bad 0.7641366874 -1.0587671921 1.018697102 0.59013988084
## 41 bad 0.7641366874 -1.0587671921 1.018697102 0.59013988084
## 42 bad 0.3507712392 -0.7154559110 -1.059672771 0.53499820416
## 43 good 1.4009534162 0.1977275307 -0.665627753 -4.40707382540
## 44 bad 0.3617225771 0.6800403302 -0.052570968 -1.75989009200
## 45 bad -2.1413294956 0.5933002723 -1.804661400 0.18374954199
## 46 bad -4.2775856693 1.2743759498 1.872111478 0.13888159422
## 47 bad 0.2331430018 -2.4845009945 -0.439431681 -0.91691623949
## 48 bad 1.2327080334 0.6702312963 -0.081953333 -0.21833863304
## 49 bad -0.9389723339 1.0727973187 -0.924320233 -0.14743930514
## 50 bad -0.6576468843 -0.4603639827 0.707222801 -0.30733969235
## 51 bad 0.5882230958 0.5507398051 -1.660006896 -0.48218201956
## 52 good -1.5606289219 0.5388697787 -1.020432496 -0.34119972626
## 53 good -1.5208686045 0.7225541985 -1.036453385 -0.39603150655
## 54 bad 0.8461291444 -2.1226970104 1.094480699 0.70933721713
## 55 good -0.2549027582 -1.4606088135 0.910215360 -0.33990777716
## 56 bad -0.7112083086 -1.5103999467 -0.811518401 0.61117926165
## 57 bad 2.3003794236 1.2845966260 -0.819968139 0.82775631487
## 58 bad 0.3128443129 -4.2061015007 1.209726327 -0.00022458761
## 59 bad -0.7529628698 -0.5696027870 -0.495893889 0.22427004189
## 60 good -0.5177435570 0.2585624607 -0.316295161 0.28558054968
## 61 bad 0.5326550505 -0.4378349865 -0.128490220 0.25920451969
## 62 bad 0.7033615032 -1.7964110059 0.149534906 -0.85777190086
## 63 good -0.6972955953 0.0408068541 -0.825624305 -0.28728632973
## 64 bad -1.5574365312 -0.6695407459 -1.132854419 -0.20704611354
## 65 bad -1.7058880733 0.4250711817 -1.462694431 1.04557972370
## 66 bad -1.7058880733 0.4250711817 -1.462694431 1.04557972370
## 67 bad -1.0600479370 0.1033969010 -0.912305305 -0.18602192792
## 68 bad -1.9571393728 0.8541823087 -1.187413625 -0.30860218640
## 69 bad 1.4023857653 0.0055827849 1.745090658 0.07345563872
## 70 good -0.7577244093 0.7222507195 -0.679518905 -1.23979126422
## 71 good -0.9999191681 -0.1609563891 -1.288287386 0.03870827837
## 72 bad -0.7695600669 -1.4259764578 -0.465585796 0.19486168013
## 73 bad -0.8562065492 -1.4149618786 -0.498962182 0.17827192118
## 74 bad -0.1462766933 -0.4179549843 -1.546862948 0.19225978910
## 75 bad 2.0689381719 -1.0901375715 1.187373173 -0.09875664726
## 76 bad 0.9132489307 0.7508055005 0.142460620 0.24431992949
## 77 bad 0.9132489307 0.7508055005 0.142460620 0.24431992949
## 78 good -2.1682947506 -0.2650487131 -1.102329678 -0.34534226854
## 79 bad -1.6932037651 -0.8767325413 -0.669534662 -0.14961697320
## 80 bad 0.8949146807 -2.2103489460 0.816299653 -1.73523651592
## 81 bad -1.3094380643 0.9937357420 -1.104818483 -0.25401241069
## 82 bad 4.2819216263 -1.5717644361 0.861477336 -7.38427695972
## 83 bad 0.1196024010 -0.9953690903 0.054496268 0.05905005385
## 84 bad 2.0986279913 -1.4962389930 -0.660432800 -6.50243373857
## 85 good -0.2790824419 0.3108440123 1.530858104 -0.47572734208
## 86 bad -1.2983952432 -0.2148402846 -0.142589421 -0.04031361251
## 87 good 3.3203917603 -1.5600848865 2.719630373 -4.54496864442
## 88 bad -0.5115931700 0.5313613659 -0.642102887 0.03017629625
## 89 bad 2.5076218099 -2.1535989796 2.192171011 -1.81411331250
## 90 bad -1.3568807729 -0.1016114134 -1.912643444 -0.02712628681
## 91 bad -0.1747656004 -3.0894037285 1.422790487 0.34572341348
## 92 good 3.3203917603 -1.5600848865 2.719630373 -4.54496864442
## 93 bad 3.4094800686 -1.5001484819 2.673904397 -4.63072758156
## 94 bad -0.5115931700 0.5313613659 -0.642102887 0.03017629625
## 95 bad -4.6345864975 -2.3596265758 0.558302447 0.02218461372
## 96 good -4.0927389504 -0.0660659790 2.460465624 0.02912184596
## 97 bad -1.8938782706 0.0742153884 -1.336429327 -0.22977309977
## 98 bad -0.3217304729 0.7657628455 -1.164621848 -0.23639219343
## 99 bad -1.5393328235 0.1727129336 -2.331551901 -0.07389170367
## 100 good -0.2192307179 -0.2111478055 -1.228185390 -0.06090248925
## 101 good -0.2904842848 0.0175302603 -0.585883379 0.06447706022
## 102 good -0.4650346765 1.0798275846 -0.462350751 0.14265510564
## 103 good -0.2192307179 -0.2111478055 -1.228185390 -0.06090248925
## 104 bad -0.3440831820 -0.6083976221 -0.938825019 0.30133662407
## 105 bad -0.7784454102 0.4797543053 -1.007620154 0.35087138724
## 106 bad -0.3440831820 -0.6083976221 -0.938825019 0.30133662407
## 107 bad 4.4437241847 -1.3807596228 0.727161096 -7.61293653968
## 108 bad -0.7903975135 -0.7645663745 -0.235562637 -1.06265699194
## 109 good 1.1210572328 -0.5781661591 0.981238174 -0.34051419156
## 110 bad 0.7757945360 -3.5153855011 0.967527753 -0.69278539053
## 111 bad 0.3891875737 -0.0999658775 -0.325156836 -1.58499481233
## 112 bad -0.0669049221 -1.2025123116 -0.343579134 -0.21340699304
## 113 bad -0.0201665498 -1.3663861778 -0.130675677 -0.19902527870
## 114 good 1.6116220721 -0.1577877280 0.206252154 0.53596345286
## 115 bad 0.3891875737 -0.0999658775 -0.325156836 -1.58499481233
## 116 good 1.1698071038 0.2752077650 0.221333992 0.17450420917
## 117 good -0.1289330701 0.1968625441 -0.733033626 0.15518084153
## 118 good -0.8434294325 0.2369320425 -1.538772782 0.15583027640
## 119 good -0.2852066931 -0.0582952225 -0.266543361 -0.38366836172
## 120 good -1.3645292367 -1.7718785457 -0.525037553 -0.41732160832
## 121 bad -1.1256407134 -2.0760545056 -2.255268708 -1.85783594839
## 122 good -0.2852066931 -0.0582952225 -0.266543361 -0.38366836172
## 123 bad -1.5075644741 0.0889943301 -2.440882582 0.18778095725
## 124 bad -1.2698331012 -0.5183610287 -1.794612569 0.24820890442
## 125 bad -0.9868665687 -1.3685541922 0.141885382 0.21746011385
## 126 bad 0.6533937651 -2.1985177474 -0.577691913 -1.05459236590
## 127 bad -2.8296888090 0.1296288257 -3.174253596 -0.12559290473
## 128 bad -2.8856553229 0.0981888206 -3.174724625 -0.09375129996
## 129 good -0.6487334280 1.3194444514 -0.499462400 -0.96778876645
## 130 bad -1.4346891605 0.9027201847 -0.649706209 -0.26666908072
## 131 bad 0.7289523106 -2.7032889287 0.682654284 -0.56406463476
## 132 bad -3.1805724630 0.2223224759 2.421065073 0.23413213480
## 133 bad -3.1805724630 0.2223224759 2.421065073 0.23413213480
## 134 good -1.9396816470 0.2606649547 -0.396355867 -0.18190985184
## 135 good -1.3976085562 -0.5701218867 -2.126196193 -0.23113939688
## 136 bad -0.0815424656 -0.8574229262 -0.730114998 -0.98379281059
## 137 bad -0.0882906684 -0.3652604368 -1.123418097 -0.83651983852
## 138 bad -0.3093735873 -0.0482406652 -0.276075786 -0.16123418246
## 139 bad -0.6507117783 -1.3046389950 -0.122395648 0.14572156777
## 140 bad -0.6374338891 -1.4350380051 -0.021926823 0.13051357028
## 141 bad -0.0815424656 -0.8574229262 -0.730114998 -0.98379281059
## 142 bad -0.0882906684 -0.3652604368 -1.123418097 -0.83651983852
## 143 good -3.8605516273 1.2536284679 3.863030729 -0.52559229199
## 144 bad -1.3641258354 1.0317351479 -0.878602300 -0.26989591548
## 145 good -3.8605516273 1.2536284679 3.863030729 -0.52559229199
## 146 bad 0.9475601351 -2.7730365240 0.897822291 -0.41664812525
## 147 bad -2.8652698825 -1.4230223107 0.204527218 -0.43005199908
## 148 bad 1.3496627748 -1.0861736358 -0.408541614 -3.00392269949
## 149 good -1.4143329865 0.4004608290 -0.359407332 0.00658037468
## 150 good 0.0462106670 0.4790205397 0.219473889 0.37324393546
## 151 good -0.0523283441 1.7030408753 -0.080540530 0.29878420123
## 152 bad 8.6506175515 -2.7574648249 1.800729783 -10.88932855283
## 153 bad -1.5478779955 -1.5179457322 0.393171182 -0.26055120873
## 154 bad -1.5478779955 -1.5179457322 0.393171182 -0.26055120873
## 155 bad 0.2712096264 -2.4323940765 2.186014766 0.97626906593
## 156 bad 0.2641282213 -2.3713456949 2.116746922 0.98939950381
## 157 bad 0.2712096264 -2.4323940765 2.186014766 0.97626906593
## 158 bad 0.2641282213 -2.3713456949 2.116746922 0.98939950381
## 159 bad -1.8498615530 -0.2657924030 -1.744475420 0.13844019781
## 160 good -1.6111528310 -1.3621788190 -0.287567256 -0.09850337590
## 161 bad -1.2859639597 -0.1020752482 -2.387342156 -0.47879793493
## 162 bad -0.1120192706 0.2955028326 -1.091642962 -1.96683223480
## 163 good -1.0742579809 0.0612309479 -0.674256589 0.00417055695
## 164 bad 0.0226183909 -3.9103759830 0.832948700 2.07562889448
## 165 bad -0.0200148627 -3.8205977594 0.834938201 2.02700186920
## 166 bad 0.4788210839 -0.9276326793 -0.041266932 -0.42790880341
## 167 bad -1.5048751507 -1.3286360275 0.101501022 -0.01644150478
## 168 bad -1.5753352298 -0.4503950280 -1.008901268 0.03942502385
## 169 good -1.9719192034 0.0918307914 -0.523064344 -0.26904430875
## 170 bad 2.8717600321 -1.3777752276 0.187296492 -7.41197390836
## 171 bad -1.7369389890 0.1110700900 -3.211799405 0.77865893245
## 172 good -0.0779569541 0.6846921202 -1.234036373 0.01316020006
## 173 good -0.0779569541 0.6846921202 -1.234036373 0.01316020006
## 174 good -1.7779866591 -0.0630882647 0.332930027 0.23657492127
## 175 bad -0.6624433130 0.5795225428 -0.727031991 0.26639036581
## 176 bad -1.0754554515 -0.4084210400 -0.051771845 -1.08159924573
## 177 bad -0.6624433130 0.5795225428 -0.727031991 0.26639036581
## 178 good -0.6169172941 0.6063455212 -0.188380394 0.07171074954
## 179 bad -1.9660188094 -0.1041103044 -2.012868394 0.03671713701
## 180 bad 0.0007361586 -0.1040756797 -1.248830435 0.32441820383
## 181 bad 0.0007361586 -0.1040756797 -1.248830435 0.32441820383
## 182 bad 2.6388040315 -2.2131697868 0.484552483 -3.76762707387
## 183 bad -1.5905338598 -0.9615511942 -1.512900980 0.17402180198
## 184 bad -1.3679174303 -0.6618629371 -0.840305677 -0.15008756713
## 185 good -1.3220828649 -0.4888865874 -0.984776678 -0.19085395431
## 186 bad 1.3202913800 -0.8618530707 0.965625637 0.26655968013
## 187 bad 0.3512441109 -0.3506194956 -0.026874805 0.22727298378
## 188 bad -1.0268162818 -0.1619057459 -1.802954475 0.32996384036
## 189 bad 0.3302101255 -1.8114664181 0.412073199 0.23326945615
## 190 bad 0.2941554682 -1.8938225846 0.513511240 0.24484673088
## 191 bad 0.6849249373 -2.0671321784 0.521704803 0.46193862183
## 192 good -1.1089200492 -0.3520094803 0.363144673 -0.02710821385
## 193 bad -0.5337173226 -2.3964100125 -0.008403848 0.01137980022
## 194 bad -0.5312619075 0.5036895295 -1.063987609 0.19335848550
## 195 bad -0.5312619075 0.5036895295 -1.063987609 0.19335848550
## 196 bad -0.0562276348 -2.0088695889 0.314097881 0.35748544003
## 197 bad -0.8386778425 -0.2667958403 -0.124239402 0.21808409987
## 198 good 3.1677209277 1.2969624330 0.455220886 -0.16563525517
## 199 good -3.6115490683 0.4216809599 1.859396749 -1.05201819509
## 200 bad -3.2161574621 0.0440734446 -1.380382872 0.38343651248
## 201 good 1.4045752635 1.5707709231 0.295210799 -0.08982999678
## 202 bad 2.4110640638 -2.7568246490 2.361786472 -1.10719589330
## 203 bad -1.0590735011 -0.1363774061 0.020706394 -1.00180255728
## 204 bad -0.3823451122 0.0576287298 -0.268412606 -0.20520947513
## 205 good -0.3370745742 0.1757676130 -0.377339321 -0.25799931266
## 206 good 3.7214910153 1.4592363916 0.189134649 0.66639319639
## 207 good 3.7214910153 1.4592363916 0.189134649 0.66639319639
## 208 bad -0.3599005233 -2.0097420793 0.470288117 0.35553049542
## 209 bad 0.4468686366 -1.2521436618 0.377293782 -0.27265175098
## 210 good 2.1823798659 1.7839213536 0.336748972 0.27551017243
## 211 good 0.8345613544 2.4791468326 1.005888843 0.27215654146
## 212 good -0.5724415472 -0.5534843117 -0.925025014 0.26340469712
## 213 good 2.3488234226 1.6546181253 -0.631976697 0.87968149412
## 214 bad -0.7419871933 -0.9075906546 0.225779825 0.95615709138
## 215 good -0.3748185502 -0.3963038840 -1.364740264 -0.55009409105
## 216 bad 0.6051692411 -2.1784091439 1.971576829 0.98954180272
## 217 bad -0.0858886685 0.2118411602 -0.421133232 -0.08337991262
## 218 bad -0.7007545958 -0.4081101409 -1.527039696 0.16327414495
## 219 bad -1.0078732101 0.2713962993 -0.531893732 0.04816089317
## 220 bad -0.0096018066 -2.3500011280 0.923899814 0.22459091008
## 221 good 0.0959528102 -0.4889943648 0.555814847 0.10836016037
## 222 bad -0.0436767558 -0.7107209150 -0.234293061 -0.36492878745
## 223 bad -1.9279513770 0.8920974717 -1.394355531 -0.31038962792
## 224 good 0.1546931895 0.3074242025 -1.420500807 0.11478499827
## 225 bad 0.0784825203 -0.2105495474 -0.442475278 0.18962387305
## 226 good -0.5985753362 -0.7205121672 1.031215491 0.45490184243
## 227 good 3.8407190827 -1.7740927186 1.471047710 -6.68733949498
## 228 bad -0.3415062675 -0.2203466687 -2.042622591 0.02353111523
## 229 good -0.5985753362 -0.7205121672 1.031215491 0.45490184243
## 230 bad -1.1369815155 0.5212516407 -0.190526266 0.47430335451
## 231 good -3.3203963427 -0.0757936171 2.304293013 -0.22424975695
## 232 good -0.9127565249 0.1998260700 -0.380719584 0.20672744962
## 233 good 0.4593750278 -0.6092240904 0.001514361 0.00483408881
## 234 bad -1.1369815155 0.5212516407 -0.190526266 0.47430335451
## 235 good -1.2030535048 -0.7100027010 -2.652528719 0.10828822229
## 236 good -1.2458929692 -0.6431129941 -1.470795190 -0.53116612530
## 237 good -1.2458929692 -0.6431129941 -1.470795190 -0.53116612530
## 238 good -1.2898703502 -0.6600768877 -1.369173139 -0.50963685070
## 239 good -1.2458929692 -0.6431129941 -1.470795190 -0.53116612530
## 240 good -1.2030535048 -0.7100027010 -2.652528719 0.10828822229
## 241 bad 2.7052732283 -0.5173138687 -0.852492591 -4.17286680333
## 242 good 2.9507230143 1.5088081765 -0.419820364 0.52275782935
## 243 good -0.4067664560 -1.9509321191 0.289170531 -0.09133166892
## 244 good 4.3834559142 1.0244430371 -0.852925268 0.69717662994
## 245 good 4.3834559142 1.0244430371 -0.852925268 0.69717662994
## 246 good -1.1971770943 0.1993028302 -1.158501420 -1.35237816888
## 247 bad -1.4173947547 -0.7706444848 -1.143003518 0.05733616069
## 248 bad -0.3856903866 -0.7153013101 -1.024847462 0.57828216428
## 249 good -1.2329684667 0.2709123286 -1.056519287 0.21699032186
## 250 good -1.1971770943 0.1993028302 -1.158501420 -1.35237816888
## 251 good 2.0070741656 0.6847972406 0.111388511 0.29170387803
## 252 good -1.4713199674 -0.2480032503 -0.754477239 -0.47062327318
## 253 bad 2.2019356945 1.2472165372 -0.717425715 1.16398314336
## 254 bad -0.0855690526 -0.7327242478 -0.784459447 -0.31887969387
## 255 good -1.4713199674 -0.2480032503 -0.754477239 -0.47062327318
## 256 bad -0.0533098400 -1.9346574382 0.037280206 0.53982805561
## 257 bad 1.3084545104 1.0492440186 -1.120405372 0.36951335810
## 258 bad -1.7824423313 -0.8827963191 -1.166606147 -0.52606675565
## 259 bad 5.1529261306 -0.8026676037 -0.045217554 -9.54495597969
## 260 good 1.8926316087 0.6152320909 0.237371065 0.24540363716
## 261 bad -0.0308251162 0.0146710746 0.080573758 -0.19985652534
## 262 bad -1.8610808098 -1.2596625754 -1.645470601 -0.62455449772
## 263 bad -1.1787535779 0.4570184918 -0.774509803 -0.00147467030
## 264 bad -0.0206363712 -0.3821007373 0.251707363 -0.22665841959
## 265 bad 3.1265999169 1.1977278273 -0.801176822 0.50823415417
## 266 good 2.2239393306 2.0678113100 -0.099502864 0.41255029252
## 267 bad -1.0016620752 -2.3310891630 -1.275051983 -0.09387104858
## 268 good 0.6745583099 1.2614870816 1.974390830 0.16977952970
## 269 good -1.1449991099 -0.1452174973 -0.959650579 -0.81993921145
## 270 good 3.3436547984 1.1777433240 0.144621192 -0.03896794886
## 271 good -0.7246593448 -1.1642909982 0.637618714 0.27723976806
## 272 good 3.3436547984 1.1777433240 0.144621192 -0.03896794886
## 273 bad 2.7705825728 -0.3676344425 0.513618740 0.98490391852
## 274 bad -0.2353655344 -0.2890596653 -1.465575454 -0.01117868063
## 275 bad 0.4274125078 -3.1885357478 0.207167289 0.81232931684
## 276 good -0.7246593448 -1.1642909982 0.637618714 0.27723976806
## 277 good -1.1449991099 -0.1452174973 -0.959650579 -0.81993921145
## 278 good 3.3436547984 1.1777433240 0.144621192 -0.03896794886
## 279 good 2.0178157279 1.7206925631 1.180147565 1.37156186073
## 280 good 1.2353152770 -0.2802935224 0.147181205 0.80960171061
## 281 good 2.9573317128 0.9536438226 -0.537878875 0.54886289639
## 282 good 3.4026121669 0.6545154258 0.286079680 -4.85054186159
## 283 bad -0.6281781983 -0.6435721908 -0.911677975 0.41802328617
## 284 good 1.2353152770 -0.2802935224 0.147181205 0.80960171061
## 285 bad 0.8561730111 -2.2154985815 -0.380878518 0.28865906966
## 286 bad 0.8561730111 -2.2154985815 -0.380878518 0.28865906966
## 287 good 3.0286257824 -0.1502117253 0.169331961 0.65916836986
## 288 good -0.4958104021 -0.8662360567 0.865903839 -0.14699345525
## 289 good 0.0532249793 -0.3528595505 0.224658612 -0.61670006096
## 290 bad 3.5725192019 -2.2594831624 0.717907300 -0.03386834850
## 291 good 0.0532249793 -0.3528595505 0.224658612 -0.61670006096
## 292 bad 3.1371058691 1.0112994010 -0.427308117 -3.30053768900
## 293 good 1.2856424772 -0.4800325018 -0.741265076 0.53890269628
## 294 good -0.5789261714 1.1149516585 -0.418246115 -0.26254227092
## 295 good 4.3735065308 -0.3833301395 -0.522339350 0.75957213270
## 296 bad 2.6545793026 0.5292317360 -1.623636024 0.80202066996
## 297 bad 1.0258089683 -1.5348285592 -0.905717943 0.82209301145
## 298 bad -1.4935260117 -0.1127164778 -1.387689506 -0.57462136996
## 299 bad -1.2479747539 -0.1891458793 -1.572282750 -0.82000527207
## 300 bad -1.6813440640 -0.2584249670 -1.433070312 -0.48953068304
## 301 good -1.1261189476 -0.2739422855 0.026893195 0.08856292779
## 302 good 1.9380230911 1.9300088095 -0.082074505 0.60700664356
## 303 bad -0.4495046383 -0.3836348280 -1.460072206 -0.22510323117
## 304 bad -0.8925519301 0.1800606564 -1.873534753 -1.74945743131
## 305 bad 1.1161123396 -1.0194415296 -0.632489682 0.01438240096
## 306 good 2.1596261591 0.4345342344 -1.689111159 0.98815381303
## 307 bad -0.4909436553 -0.1463837266 -1.060485124 0.16251837716
## 308 good 2.5740362637 0.4354964470 -1.635085721 -1.25442867282
## 309 good 2.6158522254 0.5031638710 -1.706940873 -1.30801197590
## 310 good -0.2271865925 0.8825861383 -0.249939864 0.43879699387
## 311 good 2.1596261591 0.4345342344 -1.689111159 0.98815381303
## 312 good -0.3754289614 -1.1745393404 -0.013186166 0.26204591041
## 313 good 0.7790545576 -1.4504237539 0.107405224 0.30907870635
## 314 bad 0.3709803383 -2.6541509102 0.841719064 0.88820406978
## 315 bad -0.3182426983 -0.4881112356 1.554941921 0.02188986980
## 316 good -0.5324568811 0.1456929563 1.150823524 -0.13692540278
## 317 bad 0.9857340146 -2.5089188951 0.450992120 0.54872824372
## 318 good 0.0682013843 -1.8782973065 -0.384339786 0.49317278664
## 319 good 0.8065195761 -0.4656527998 0.295445854 0.61353113000
## 320 good 0.0682013843 -1.8782973065 -0.384339786 0.49317278664
## 321 good 0.8065195761 -0.4656527998 0.295445854 0.61353113000
## 322 bad 1.0913378105 -2.0354317302 -0.409942647 0.67803115713
## 323 bad -0.5786493035 0.2167498626 -1.809135596 -0.19560202375
## 324 good 1.5382273586 -1.9130745536 0.187929620 0.40094434656
## 325 good 2.7692650017 -2.4094156101 -0.952441247 2.86340778602
## 326 good 2.7692650017 -2.4094156101 -0.952441247 2.86340778602
## 327 good 3.0873061094 1.5244026295 -0.146096844 0.28263787309
## 328 bad 2.0063423101 1.3870186291 0.212858989 0.23499450798
## 329 good 3.8459792045 1.2411981329 -0.901255327 1.14584747120
## 330 bad 1.5748601207 1.0109794179 -1.274910301 0.69456749154
## 331 good 2.1147976115 1.4708374127 1.070465615 0.39493425827
## 332 good 2.1147976115 1.4708374127 1.070465615 0.39493425827
## 333 good 0.1785995334 -2.0355976254 0.083084242 0.82511047400
## 334 bad -0.3865884068 0.1765149707 -0.275551372 -0.02457954583
## 335 good -1.1525342309 0.2459690376 0.173794194 -0.25628043310
## 336 good 2.6709356731 1.1420884181 -0.394738770 -0.06956411567
## 337 good -0.1732675216 2.5748399544 1.096586602 0.37573952362
## 338 bad 0.1843473050 -0.8449499358 0.824984734 0.34135017399
## 339 good 3.6999638933 -1.1138174652 1.455210350 0.07211501851
## 340 good 4.3587223617 0.9843570829 0.596582217 -1.33419682861
## 341 good 3.8788860031 0.8148768356 -0.316394900 -0.63618007275
## 342 good 1.8071734923 1.8525298080 0.314741070 0.17043878924
## 343 good 1.9883293821 1.2391147698 -0.818401624 -0.45638016149
## 344 good 1.9883293821 1.2391147698 -0.818401624 -0.45638016149
## 345 good 2.9957675795 0.7404242177 -1.050449313 0.57154566981
## 346 bad -1.7610389940 -2.1304390246 0.432494332 -0.55496694391
## 347 good -2.4211980941 0.3066760530 0.596911779 -0.84956866777
## 348 good 4.0617329780 2.0550218582 0.209119330 0.33309558196
## 349 good 1.6795446529 -0.1711743458 -0.185761320 -0.58397579091
## 350 good 0.0815424640 -0.6831412199 -1.998025068 -0.74956613997
## 351 good 1.8206498952 -0.6078926362 -1.058956134 -0.78075893598
## 352 good 0.0657245690 -0.6795199044 -2.101914865 -0.70129197444
## 353 bad -0.5213494466 -0.0502215478 -1.750354367 -0.68975641327
## 354 bad 4.3839964082 -0.3482713945 1.321090917 1.48891674944
## 355 good -1.3511585954 -1.3168735876 4.759621506 0.00633763290
## 356 good -2.7049351652 0.5991376985 0.435907479 -0.01159825690
## 357 bad 2.7225920015 -0.4353657209 1.045279153 0.51809756364
## 358 good 2.1054762294 1.3854694986 0.880428988 0.03363229895
## 359 good 3.6377038912 0.8377996131 -0.342706973 0.13910232842
## 360 good 3.7402299853 0.4302034260 -0.803047229 1.01406580678
## 361 bad 0.2954347898 -1.1666188282 -0.802429372 -0.59074032858
## 362 good 0.8135790155 -0.4684306533 0.420712453 -0.59343941708
## 363 bad 2.9760271811 0.6878342217 -1.266138973 1.25627974850
## 364 bad 3.6706958376 1.5605552717 -0.643746882 0.21194250638
## 365 good 4.3892799173 -0.4503455074 -1.362992289 1.81118378264
## 366 good 1.9808037810 1.5183823854 0.674061047 -0.37018872326
## 367 good 4.3892799173 -0.4503455074 -1.362992289 1.81118378264
## 368 bad 2.5916278305 0.0716678060 -1.462199613 0.55677988430
## 369 bad 2.8673289530 -0.8652187319 -0.321879502 -0.25082184915
## 370 good 1.9119913874 2.4615909020 1.754336206 -0.96124840574
## 371 bad -1.8490125711 -2.0189931519 0.105734660 -0.39393297439
## 372 good 0.7865851323 0.8349187550 0.093793796 -0.61162868822
## 373 good 1.3362106119 0.4649802812 1.941134627 -0.79134383470
## 374 bad 0.3247468020 -0.7101202336 -0.555684545 -0.62648032792
## 375 good 4.7876029568 0.6560572904 -0.509631301 1.19125829563
## 376 good 3.5932507026 1.4263199067 0.113438332 0.23871835134
## 377 good 3.0130182802 0.1496000349 0.779623053 -0.25839110180
## 378 good 1.9119913874 2.4615909020 1.754336206 -0.96124840574
## 379 good 3.2331876694 1.3857565644 1.027684364 0.85406099019
## 380 good 0.6693528537 -0.3937882231 0.884188641 0.04852957703
## 381 good 1.3554499227 0.8141820465 0.068269032 -0.42162195351
## 382 good 4.5920898329 0.0894409184 -0.436768906 0.97211130738
## 383 good 1.3554499227 0.8141820465 0.068269032 -0.42162195351
## 384 good 1.3554499227 0.8141820465 0.068269032 -0.42162195351
## 385 bad 0.0047823430 -0.9839074402 -0.155541444 -0.42969628078
## 386 good -0.7391286994 -0.3371219471 -1.059562820 -0.65542154921
## 387 good -0.0154886172 -0.9311242300 -0.352122334 -0.47579252692
## 388 good -0.5647850001 -0.5069134533 -1.146704592 0.17222233662
## 389 good 0.0275182472 -0.8764236083 -0.101791867 -0.46120923142
## 390 good 1.4388821018 -0.0493068921 -0.085461206 0.06897719132
## 391 good -3.6346031116 0.4457253678 1.599668671 -0.90108436819
## 392 good 4.5920898329 0.0894409184 -0.436768906 0.97211130738
## 393 bad 1.5943891646 0.9549144307 -0.942884395 0.68738653623
## 394 bad 1.1997505915 -1.1439819432 -0.547470474 -0.11035899967
## 395 bad 3.2669997767 0.8797254024 -1.624558719 1.41256059867
## 396 good 3.1753769295 1.6590597165 0.803436903 2.27155414888
## 397 bad -0.7935544314 -6.2206587322 1.648028062 1.41242831261
## 398 good 2.3157778903 0.4008152171 -0.254755388 0.81482064994
## 399 good 2.3157778903 0.4008152171 -0.254755388 0.81482064994
## 400 bad 0.0193242204 -0.1751750609 -1.611871090 0.34820203260
## 401 bad -0.7935544314 -6.2206587322 1.648028062 1.41242831261
## 402 good -0.6203987865 0.9143858304 1.014250322 0.72105698445
## 403 good 2.9954685936 -0.3078854902 -0.224832345 1.18596443172
## 404 good 2.2776909622 0.3154014205 -0.958632669 0.85271804610
## 405 bad -0.6129961912 -0.6411603366 -1.625927439 0.01559684162
## 406 good 0.6127886296 1.3477290738 0.385336600 -0.09814348086
## 407 good 1.3366507824 1.0960110122 -0.174357465 0.79621594623
## 408 good 3.1079036467 0.9002242401 -0.177431058 1.11022675575
## 409 good 2.4571333684 1.4782236132 0.278815800 -0.52279455241
## 410 bad 2.8640470336 -0.7982561130 -0.092945103 1.79193263116
## 411 good 0.6983753098 -1.4910271681 0.300014640 0.54057270463
## 412 bad 0.7179048121 -1.2025100672 0.046332402 0.50168200345
## 413 bad -0.6883471565 -1.2747914512 -0.893169808 -0.69965122498
## 414 good 2.3208606124 0.8968074733 0.616459211 1.05647790651
## 415 bad 0.8290338411 -2.1626824650 0.387889393 0.44114880504
## 416 bad 1.9658957174 -4.3356576166 0.508905107 -0.28862834649
## 417 good 1.7342678135 1.8741512420 0.178826118 0.14322147935
## 418 bad -1.7268536455 -2.2767431200 1.221845870 0.16435240429
## 419 good 2.8272604901 1.1970934917 -0.698628341 -0.11863358398
## 420 bad -2.2384733786 -1.7587245427 -0.279164132 -0.27241347626
## 421 good 0.5104560598 -0.7408101754 1.515032548 0.17384191430
## 422 good -2.9379453288 -0.6180393662 0.782025357 0.34212351393
## 423 bad -1.9078394555 -0.4415537933 -0.898096072 0.78925429934
## 424 good 2.3092629911 1.9007178239 0.603230873 -0.00766889244
## 425 bad -1.9078394555 -0.4415537933 -0.898096072 0.78925429934
## 426 good -2.9379453288 -0.6180393662 0.782025357 0.34212351393
## 427 good -2.6698811458 0.0869264716 0.293574627 0.59810088010
## 428 good 0.2005993874 0.3299998982 -1.822970851 0.47447648715
## 429 bad 0.6336576249 0.4770612131 -1.269993528 0.16005510392
## 430 good 2.8248308132 0.0720427227 -1.619267204 0.98553061473
## 431 good 2.3092629911 1.9007178239 0.603230873 -0.00766889244
## 432 bad 0.2126650030 -1.0306199421 -0.362042804 0.33445907522
## 433 good 2.5647785520 2.4414111210 1.370273739 0.94756801348
## 434 bad 3.2132819200 0.9393370557 -1.602877038 1.32260622089
## 435 good 2.8499349677 -0.4765674275 0.463659179 0.25702835796
## 436 bad 3.2132819200 0.9393370557 -1.602877038 1.32260622089
## 437 good -0.6253324578 -1.3639456918 0.665574960 0.46633707190
## 438 good 2.6750164540 1.5607372666 -0.033354351 1.25095086352
## 439 good 2.8499349677 -0.4765674275 0.463659179 0.25702835796
## 440 bad -0.5519972372 0.1529855894 -0.986882478 -0.29689142239
## 441 good 4.4104777611 1.4443593791 -0.296928748 0.48609716620
## 442 good 3.0487796426 1.7073177491 -0.051316048 1.01163289797
## 443 good 5.5353026325 0.3866010798 -1.606180710 1.78094859451
## 444 good 1.0486353799 1.6079465809 0.457060782 0.67437408982
## 445 good -3.1366432904 1.8742317381 1.074781043 -0.99296408941
## 446 good 0.1004276569 0.1228777760 -2.052628005 0.39236422724
## 447 bad 3.3902207995 -0.7275450494 0.926682828 0.98209632933
## 448 bad 0.8056683330 1.4157262296 -0.336349950 -0.69982504023
## 449 good 0.1608407480 0.7200970876 -0.999503845 -0.07568020374
## 450 good 3.2824688943 0.6413081726 0.075010522 1.09868837696
## 451 good 3.2824688943 0.6413081726 0.075010522 1.09868837696
## 452 good 3.9212462064 -0.2625294107 -0.691586474 -6.02467090048
## 453 good -1.5119182559 -0.2383788464 -0.541770133 -0.43769452687
## 454 good 2.5537422970 1.5576041243 0.040540345 0.65924282952
## 455 bad -0.8503685741 0.5474157617 1.993179961 0.18163312839
## 456 good 2.3663151025 1.6456979738 0.532848343 1.59492216756
## 457 bad 0.5148554636 0.7202066952 -1.263552855 0.26955782077
## 458 bad 0.4877010264 -1.7431759000 -0.554747902 0.37667165368
## 459 good 2.5537422970 1.5576041243 0.040540345 0.65924282952
## 460 bad 2.7506550682 -0.2159928739 -1.647034384 1.14193425951
## 461 good 0.8213194793 1.1584287596 0.342815131 0.56525199099
## 462 bad 0.2756233733 0.1799343443 -1.805296884 0.05183617767
## 463 bad 2.2887459152 2.0284317211 0.973378491 0.96375805579
## 464 bad 1.5422026457 -2.3172803865 0.336910966 0.26326782957
## 465 good 3.3111852800 1.2512634309 -1.087008458 0.37390869189
## 466 bad 1.9520019204 0.4218592993 -0.332841451 -0.26143722095
## 467 good 1.5025887554 0.3472039878 0.625440351 0.53235597560
## 468 good -0.0783413905 2.8399288289 1.932788973 0.22616948402
## 469 good 3.2260222552 0.9339909561 -1.216015780 0.71241657369
## 470 bad -1.2199010844 -1.3681814617 -0.458457393 -0.16556092112
## 471 bad 3.7806147396 0.6277132514 0.006173020 1.23236948809
## 472 good 0.9663272335 0.0210395840 0.923033051 0.28707123940
## 473 good 3.2880736770 -0.3997064154 0.742495531 0.59666328154
## 474 bad 1.5885087723 1.9034063574 0.144195316 0.18885695837
## 475 good 2.4564707668 1.0735234679 -0.118489214 -0.40901380073
## 476 bad 0.9269953314 0.4035933039 -1.788491295 0.29052422733
## 477 bad 1.4100404940 1.2650830425 -0.488178675 -0.06972975077
## 478 good 2.4585284773 2.0553046977 0.849568318 -0.75210743794
## 479 bad 0.9269953314 0.4035933039 -1.788491295 0.29052422733
## 480 good 0.4882581689 0.1491076758 -1.608589356 0.11780196204
## 481 bad 4.4331012560 -2.0928771644 -0.721202888 4.47143645967
## 482 good 1.9260510625 2.1823343795 1.188763955 -0.27712598411
## 483 bad 3.7219672565 0.9895601332 -0.424824664 -1.79529710588
## 484 bad 3.7566418720 0.9504703020 -0.355095548 -1.81955647052
## 485 good 2.4835011368 2.1461456165 1.175743569 0.83128027845
## 486 bad 0.7730634181 0.9228737640 -1.722259921 1.10493499157
## 487 bad 0.7730634181 0.9228737640 -1.722259921 1.10493499157
## 488 good 0.7754386549 0.9760264566 -1.961999777 1.30209460648
## 489 good 2.8427578376 -0.8129348741 1.630258464 0.41154821939
## 490 good 1.1325865363 0.7845255627 -0.207017076 0.24715392409
## 491 good 0.2546396254 -1.0404219646 -0.427233125 0.40140711476
## 492 good 0.2742794474 2.3487113011 1.985200011 0.43109358909
## 493 good 0.3838318675 2.3454651832 2.371748430 0.11018430304
## 494 good 0.2609030033 -1.2165531107 0.378931029 0.39558636366
## 495 good -1.0259683220 -1.5295354305 2.733839127 2.34473377532
## 496 good 1.9453179863 1.8886616183 -0.010244977 0.81466235404
## 497 good -0.1323202712 -0.3823065302 -1.057917588 -0.14859496569
## 498 bad 0.0746504001 -1.9362243090 2.914431100 -0.27235342346
## 499 good 1.9453179863 1.8886616183 -0.010244977 0.81466235404
## 500 good 0.2609030033 -1.2165531107 0.378931029 0.39558636366
## 501 good -0.1323202712 -0.3823065302 -1.057917588 -0.14859496569
## 502 good 3.0740056348 -1.3634390467 2.703174879 1.63675072753
## 503 good 3.0740056348 -1.3634390467 2.703174879 1.63675072753
## 504 good 2.4001570896 1.8890827105 0.748935182 -0.72342557163
## 505 good 2.2312431689 1.9025707181 0.696680608 -0.79260643000
## 506 good 1.8009790711 1.8585460907 1.070296602 0.52284953911
## 507 good 2.1319952494 1.9426107415 0.712249023 -0.66375182611
## 508 good 2.5085956265 0.5836603547 -1.922981417 0.36357285482
## 509 good 1.3462990796 -1.0767299775 -0.360117088 0.45114438365
## 510 good 3.9267610227 0.8665317837 1.148874552 1.30682703002
## 511 bad 3.0233474835 -0.3133108471 -0.265408200 1.57498447908
## 512 good 1.3462990796 -1.0767299775 -0.360117088 0.45114438365
## 513 good 2.8208511914 0.1046897240 -0.107600881 -0.66017284285
## 514 good 2.2003808022 1.8887903830 0.010615811 0.25608043032
## 515 good 2.2003808022 1.8887903830 0.010615811 0.25608043032
## 516 bad 2.6435464998 -4.3351146220 1.425115050 -0.60636485454
## 517 good 2.8782225235 1.0715309886 -0.869402308 1.11273986335
## 518 bad 2.6338559267 -0.0925382175 -2.558074438 -1.35761871772
## 519 good 1.9552765317 1.6756219860 1.045707398 0.71083321275
## 520 bad 0.3565170016 -1.5521237552 2.777978101 -0.17456530233
## 521 good 1.7066772449 0.6502216970 0.695569452 -0.27312558286
## 522 bad 0.3170780339 -0.1838128652 -0.231144023 -0.07947428815
## 523 bad 1.2406867883 -2.9396943347 2.797535388 -0.91316565833
## 524 bad 1.3771074539 -2.8168259776 1.510554208 0.82144234784
## 525 bad 1.3844493799 -1.5860124630 0.795831716 0.36539923690
## 526 bad 1.3410589741 -1.4890767514 -0.667505342 0.27672529692
## 527 bad 0.3565170016 -1.5521237552 2.777978101 -0.17456530233
## 528 good 0.0726602276 -0.7734296276 2.535605447 -0.35307141327
## 529 good 1.2351886623 -1.4710981677 0.334270873 -0.04309644069
## 530 bad 0.9444469397 0.2354909011 -1.730966670 0.43108821915
## 531 good 1.0135709739 1.3817156700 -0.195670955 -0.32914636976
## 532 bad 2.9374344615 0.3274232994 -0.672340042 0.97580660872
## 533 bad 2.9374344615 0.3274232994 -0.672340042 0.97580660872
## 534 good 0.4731931599 1.9145315696 1.527776604 0.60559676511
## 535 good 1.2182102551 -0.1055728854 -1.258369534 0.61030364514
## 536 good 1.0135709739 1.3817156700 -0.195670955 -0.32914636976
## 537 bad 0.9444469397 0.2354909011 -1.730966670 0.43108821915
## 538 good -0.5203604485 0.7810462159 -1.319795528 -0.60842000130
## 539 good 3.4414459596 0.9058918749 -0.067308435 2.48288553643
## 540 bad 3.0257055388 1.0157157141 -0.397563123 1.49475961291
## 541 bad 0.8966116493 -1.0124472154 -0.786590187 0.15920777414
## 542 good 2.0027718933 0.7170936153 -0.096014840 1.54528761871
## 543 bad 0.3167933675 0.7042936737 -1.669091320 0.23551005926
## 544 good 1.8242062565 -0.2305351720 -0.697618350 0.43605306546
## 545 good 5.5003604339 1.1049505130 -1.618413361 0.68616083521
## 546 bad 1.8234269211 -2.5046429849 0.794543147 0.49456501969
## 547 good -0.2214901369 0.1259488350 -0.911877193 -0.53763050857
## 548 good 2.1861450699 1.5530286241 0.122776135 0.25276886992
## 549 good 3.0835287410 -0.5268502942 0.717314650 0.85133531344
## 550 good 1.3531187498 0.4546680757 -1.294141754 -1.12907006593
## 551 good -1.4168217862 0.3239126645 -1.057525812 -0.14945596596
## 552 good 1.1676454978 -0.0397839517 -0.288896680 -0.00056148345
## 553 good 1.3578562148 -0.0376318356 -0.398005435 -0.06231002561
## 554 bad -4.0639695509 -1.5940326018 1.237827982 -0.32644286596
## 555 bad 5.0903446976 0.2849233122 -1.762554933 1.66211071397
## 556 bad 5.0903446976 0.2849233122 -1.762554933 1.66211071397
## 557 good 2.7259424294 0.6600007083 -0.668946122 1.26475992822
## 558 bad 5.1184484969 0.2912700454 -1.769636768 1.67529880516
## 559 good 2.7259424294 0.6600007083 -0.668946122 1.26475992822
## 560 good 3.0619207947 0.8679622262 -0.107950003 1.87681967968
## 561 bad 2.4566123568 1.3497088067 -1.148852096 1.44614728036
## 562 bad 1.2598392395 -1.9695075053 0.704865624 0.64468032668
## 563 bad 1.7436745235 -2.8522322466 0.886727791 0.49299017443
## 564 good 0.6937115471 -0.4089000309 1.139531195 0.28200077165
## 565 good 3.0619207947 0.8679622262 -0.107950003 1.87681967968
## 566 bad 2.4566123568 1.3497088067 -1.148852096 1.44614728036
## 567 good 0.9750974328 -0.4341306533 -2.672124085 -1.84426182768
## 568 good 0.9750974328 -0.4341306533 -2.672124085 -1.84426182768
## 569 good 2.3670218236 0.6271726688 -0.852457079 -2.24914731869
## 570 good -1.9079911544 0.7622662171 1.481007283 -0.16531256500
## 571 good 2.6843706293 0.8307022563 0.281334311 0.57765868872
## 572 good -1.9079911544 0.7622662171 1.481007283 -0.16531256500
## 573 bad 2.0098230436 1.2641757791 0.107981902 0.80712357097
## 574 bad 1.4051419287 -0.1747039620 -1.064538067 0.91531302974
## 575 good 2.4094212911 -0.6848251362 0.518744216 0.87133903271
## 576 good 3.0386193461 -0.2102714466 0.818773095 0.79290773813
## 577 bad 0.4492157693 0.8475169072 -1.444210694 0.34838103824
## 578 bad 1.1698659635 -1.4486190735 0.432103294 0.61758293100
## 579 bad 1.1485427391 -1.3447108042 0.438214832 0.63142194547
## 580 good 1.8136205644 0.4430688104 -0.036158861 1.22959693577
## 581 bad 2.5053310512 0.8538955100 -2.125114730 1.36672737829
## 582 bad 2.5053310512 0.8538955100 -2.125114730 1.36672737829
## 583 bad 2.3099668814 0.7103139047 -2.193525770 1.34613376214
## 584 good 2.9384057937 1.3994101380 -0.385226521 0.65922882621
## 585 good 2.6409076451 -2.2446060195 2.127525254 0.99860796196
## 586 good -0.3281823776 0.0466136990 -0.891732878 -0.20866525190
## 587 good 3.0698464544 0.5378164081 0.898833222 -0.48575636871
## 588 bad -0.4137554716 -1.8203625877 -0.548880974 -0.54143468807
## 589 good -3.5323125244 1.8277059351 3.423481538 -0.45230941369
## 590 good 2.2877256712 1.7980130167 -0.062683264 0.59613797893
## 591 bad 1.0588377991 -0.9381101168 0.114525140 0.71504172469
## 592 good -0.6458707802 -0.7086632387 2.967712369 0.29853566806
## 593 bad 1.0588377991 -0.9381101168 0.114525140 0.71504172469
## 594 bad 2.2448292655 -0.3934909637 -1.321214570 0.90857477740
## 595 bad -0.7072350554 -0.9920649046 -1.278946946 0.13261826943
## 596 bad 1.6987890950 -3.5665847850 0.333918176 2.10541625761
## 597 good 3.0816260835 0.9723742884 -1.453879104 1.18658073646
## 598 good 2.4441081104 1.2847359315 -1.286380836 0.71758428429
## 599 good -0.1876797616 0.5316224336 -1.490751610 0.41737062718
## 600 good 3.2686798844 0.2614656674 -1.821926681 0.61988089418
## 601 bad -0.7750688861 0.1970914359 -2.010783183 0.13907012203
## 602 good 3.3677954563 0.1087584838 -1.696396575 1.39728533969
## 603 bad -1.1296098743 -0.1969752998 -2.629832387 0.09442249291
## 604 good 3.3677954563 0.1087584838 -1.696396575 1.39728533969
## 605 good 0.5167240728 -1.1682713576 -1.205939327 0.02290969801
## 606 good -0.0421897513 -0.0150273222 -1.919230483 0.12857389537
## 607 good 1.1372793216 1.4133356212 1.069062242 0.81840255988
## 608 good 0.7136544372 -0.7220955768 0.332428018 0.88580820003
## 609 good 2.1477209667 -1.3811931087 -1.278327329 1.17647043761
## 610 good -1.9138056976 1.0758989135 1.507381190 0.72454555565
## 611 bad 1.1725698019 -0.5918086749 0.515834326 0.79126385725
## 612 bad 3.9229649729 0.9453744887 -1.816421013 1.49425593622
## 613 good -1.2215029021 -0.3136535325 -0.864314136 -0.04821232013
## 614 bad 0.3909134134 1.0352270962 -0.002112744 -0.20017447969
## 615 good 2.3414616671 -1.1042262304 0.292695918 -3.23573409160
## 616 bad 0.7541133639 -1.4536420119 -0.180131718 0.84462337498
## 617 bad 0.7541133639 -1.4536420119 -0.180131718 0.84462337498
## 618 good 3.2315705622 0.8931537422 0.155901690 -0.21145504004
## 619 bad 3.0988015816 0.8909876825 -1.388764634 0.19318067082
## 620 bad 2.7495032393 0.9764734848 -1.080284783 -0.02981989725
## 621 bad 0.3238192283 -1.8000424781 -0.036081365 0.55590128213
## 622 bad 0.2413929414 -1.6127084858 -0.235576387 0.45252086771
## 623 bad 0.7892964765 0.2901628615 -1.539308790 0.37238345362
## 624 good -0.2895111666 0.3073576808 1.481137233 -0.51649768352
## 625 bad -0.5074863403 -2.1526122708 -0.631431308 -0.00653140529
## 626 bad -0.5074863403 -2.1526122708 -0.631431308 -0.00653140529
## 627 bad 0.3023997610 0.2725123581 -2.204928417 0.30807895711
## 628 bad 0.3023997610 0.2725123581 -2.204928417 0.30807895711
## 629 good 0.4546272277 0.1542494882 -1.369526359 -0.01997405782
## 630 bad -0.1502794070 -1.5694927403 -0.472689677 -0.52736259185
## 631 good 0.4546272277 0.1542494882 -1.369526359 -0.01997405782
## 632 bad 2.3302690054 1.0531914723 -0.779982173 0.38762810003
## 633 good -0.5638691680 -0.1950537150 0.351160527 0.31334969628
## 634 bad 0.4840736254 -1.4360569036 -1.357682559 0.59095124133
## 635 bad -0.1916634547 -2.3798771631 1.673139605 0.33292542794
## 636 bad -1.4545428918 -0.6591694516 -1.251432369 -0.01835410747
## 637 bad 0.2829088144 -3.3937236990 -0.305309439 0.56982273355
## 638 bad 0.1791229257 -3.3920481232 -0.286924964 0.54503591865
## 639 good -0.9912326773 -0.0601414360 -1.876519791 -1.28143400791
## 640 good 1.9299475446 0.1061397954 2.134399398 -1.89133372433
## 641 bad 1.0728456949 -0.2490290038 -0.875396993 0.66590500004
## 642 bad 0.6986170436 -1.0661692437 -0.436574624 0.62720001058
## 643 bad 1.0728456949 -0.2490290038 -0.875396993 0.66590500004
## 644 bad 0.6986170436 -1.0661692437 -0.436574624 0.62720001058
## 645 bad 1.0728456949 -0.2490290038 -0.875396993 0.66590500004
## 646 good 0.0516123430 -0.2621901346 -1.418399701 0.26894864904
## 647 bad -0.9522409339 0.0383656789 -1.419352619 -0.25354126916
## 648 bad -1.3571472887 0.7219166746 -1.730720259 0.14543706978
## 649 good -0.0148188811 1.0717513036 0.200677111 0.43111362651
## 650 good 0.0730832368 -2.6573057544 2.722109221 2.05557382458
## 651 bad 2.5930748917 0.4938675199 -1.418055268 0.58630952189
## 652 bad 0.5235881412 -3.5720457245 0.433678017 0.42703109061
## 653 bad 4.7092275291 0.8959366947 3.184346232 2.46357501162
## 654 good 1.7120439632 1.6677641690 0.681316961 1.14173455132
## 655 bad 1.2847153310 0.5578790746 -1.178664347 0.88132094838
## 656 bad 1.4348163962 -1.2350541303 -1.269156460 0.63000887231
## 657 bad 2.5930748917 0.4938675199 -1.418055268 0.58630952189
## 658 good 3.0161878260 0.4123186478 0.093351533 0.60042501714
## 659 good -0.5469547541 0.7051513810 -1.117018673 -0.94619563855
## 660 bad -1.5265409477 0.1289505064 -1.560603970 0.26852456258
## 661 good -0.5469547541 0.7051513810 -1.117018673 -0.94619563855
## 662 bad 0.2240558964 -0.1811042462 -0.605005806 -0.20232177138
## 663 good -0.9086472195 0.2443950141 -1.160543638 -0.57452329106
## 664 good 2.1680971829 1.8938834033 -0.125001992 0.27537163094
## 665 bad 3.3047694277 0.1616971779 -0.436451072 0.71640819057
## 666 bad 0.5128181265 -0.9324151090 -0.824690031 0.36486404139
## 667 good 1.4488820420 0.3610926701 -1.594451732 -1.92655253693
## 668 good 3.0618655706 1.0870477750 -1.306922621 0.44410587125
## 669 bad 0.6594202008 0.5812274229 -0.505158091 0.85229103340
## 670 good 3.0618655706 1.0870477750 -1.306922621 0.44410587125
## 671 bad -0.4756540111 -0.6281254216 0.771211297 0.09266442748
## 672 bad -0.3553170343 0.5851726190 -2.069386611 0.10394014698
## 673 bad 0.1911368607 -3.9274852090 -1.051840435 0.48626405760
## 674 bad -0.3553170343 0.5851726190 -2.069386611 0.10394014698
## 675 good 2.2961796834 0.8852852747 -1.215274977 0.31165407990
## 676 bad 1.0806917507 0.6480932614 -0.344494443 0.59249017280
## 677 good 2.2961796834 0.8852852747 -1.215274977 0.31165407990
## 678 bad -0.9156804674 -0.3861187797 -1.920865866 0.43267404548
## 679 bad -1.1359334190 -3.0260560531 0.152366105 0.60377407449
## 680 bad 1.9501037512 0.0286337856 0.310600958 1.45515400970
## 681 bad 3.5076923844 0.0460878142 -1.496887065 1.60875580466
## 682 good -0.1468564617 0.3623565287 -0.351064880 0.24342311841
## 683 bad 0.2196113221 -1.1677141208 0.288580126 0.63535870112
## 684 bad -0.9502950855 0.9855350322 -1.159378195 0.62828716172
## 685 bad 0.1618231077 -3.8148758724 -0.356061574 0.85325714789
## 686 bad -0.9502950855 0.9855350322 -1.159378195 0.62828716172
## 687 bad -1.1278954647 -0.4145194894 -1.210725666 -0.15481333122
## 688 bad 0.7754448806 -0.6525112146 -1.554968991 0.47774779267
## 689 bad -1.7295760640 0.7944396441 -2.028813101 0.56062642001
## 690 bad 1.7667765702 0.9547992276 -0.245219992 -2.44649296925
## 691 bad -2.6796319383 -0.4642430756 -2.504470516 0.12655043131
## 692 bad -0.2411098372 -1.8332354867 -1.739063674 0.58509051068
## 693 bad 4.0863559656 -1.4940570453 -0.269216620 -6.43658443258
## 694 bad 0.7945698336 -2.0281077996 0.298648156 0.50942781670
## 695 bad 0.7795840176 -2.2022577126 0.436502989 0.53121337975
## 696 good -4.7570170106 1.4369202681 2.189013618 -0.08459318569
## 697 good -1.5027212153 0.0487247824 -1.454003561 -0.29627040232
## 698 good -1.5027212153 0.0487247824 -1.454003561 -0.29627040232
## 699 bad 0.8850818655 -1.4360803653 -1.032796630 0.96841288823
## 700 good 2.6020749372 1.7187550420 -0.551912698 1.01447745159
## 701 good 1.2548205104 -2.0679538702 -1.004852046 0.88877166813
## 702 good -1.5027212153 0.0487247824 -1.454003561 -0.29627040232
## 703 good -1.5210472578 0.0349734940 -1.417802392 -0.33391228334
## 704 bad 0.6280637866 -0.9244222772 0.963705642 0.13996594548
## 705 bad -0.5386257098 0.4190722192 -2.535528437 0.12281301887
## 706 bad -0.5060846684 -1.8895659121 -2.168654744 1.54146245996
## 707 bad -1.9090352061 0.1738565314 -1.592129691 -0.17808898608
## 708 bad -0.8258935560 0.0489786110 -0.134911740 0.60121873100
## 709 good -1.0439575229 0.8366195977 0.118609601 0.31837686813
## 710 good 1.4297631105 0.8693927557 -0.052581798 0.53345577663
## 711 bad 1.3480066416 -1.7656872047 -1.302070492 0.78724690910
## 712 bad 0.8701260759 -1.9395265406 -0.390919705 0.53595492703
## 713 bad -0.5155006970 -0.9009758212 -2.179997831 0.85313620783
## 714 bad 0.1142581515 0.0250194720 -0.763367193 0.73402447395
## 715 bad 0.3555203142 -1.3930239000 -0.858634139 0.84728728015
## 716 good -0.6215288318 -0.1758650209 -0.963930447 0.54477224236
## 717 bad 0.1142581515 0.0250194720 -0.763367193 0.73402447395
## 718 bad -0.2946467984 -0.0807556821 -0.468531187 0.23235091965
## 719 bad -0.4353694587 0.1607041425 -1.789236246 0.47824006179
## 720 bad -1.3497063692 -1.2521233860 -1.145898567 0.41489146300
## 721 bad -0.4353694587 0.1607041425 -1.789236246 0.47824006179
## 722 bad 1.0417457870 -2.6063687073 0.325500876 0.62241845223
## 723 bad -0.2622641931 -0.2032905457 -0.261171089 0.13986472340
## 724 bad 2.2930946569 -2.0035345098 3.411719265 -2.74987260875
## 725 bad -2.3695525657 0.3160554988 -1.863568710 -0.15293130990
## 726 bad -0.1248637338 0.6818254028 -1.573715146 0.60713424569
## 727 good -0.5077890181 -0.7122041184 -0.581428819 0.06699577222
## 728 bad -1.5866951178 0.6543851620 -1.486507858 -0.51619801497
## 729 bad -1.5866951178 0.6543851620 -1.486507858 -0.51619801497
## 730 good -3.2959842750 -0.6578918888 0.798499481 0.61959847452
## 731 bad 3.3703914361 -0.6673383200 -1.180127138 -4.02649892124
## 732 bad -0.6431042654 0.3872298743 -2.006124166 0.83671798931
## 733 bad -1.7307727314 -0.2149417794 -2.163916509 -0.09621509320
## 734 bad -0.4942352456 -0.8854014949 0.010883639 0.24067189907
## 735 bad -0.4854639045 -0.1985112179 -1.340591497 0.05265765365
## 736 bad -1.1760580802 -0.4853739321 -2.391731970 -0.48791632950
## 737 bad -1.1760580802 -0.4853739321 -2.391731970 -0.48791632950
## 738 good -0.4484857621 -1.5215577240 -1.311676075 0.00452611872
## 739 bad 1.1452016678 -2.1885891150 0.081800285 0.53595044046
## 740 bad -0.3011096966 -1.0991033211 -1.736118973 0.13710674595
## 741 good -0.6914087775 0.5130703840 -0.263431934 0.50338644341
## 742 bad 0.6551926626 -2.6407432456 0.525995803 0.68337305693
## 743 bad -1.6462958348 0.0562626108 -1.564649195 -0.36649670840
## 744 bad 2.9949658078 -1.4455699824 0.258577314 1.34064391571
## 745 bad 2.7799871931 -1.4272277087 -0.103042717 1.23300719237
## 746 good -0.8001888147 0.0982029029 -0.710714480 -0.31594094398
## 747 good 0.5931393943 -0.2825939917 -0.563578616 0.74728483789
## 748 bad 0.7604462726 -0.4971319750 -0.079292449 0.84000005429
## 749 good -0.8479286141 0.0948144405 -0.610744088 -0.29822901984
## 750 good -0.8001888147 0.0982029029 -0.710714480 -0.31594094398
## 751 bad -0.2675412924 -0.8562008690 -1.334427175 0.30928982527
## 752 bad -0.2675412924 -0.8562008690 -1.334427175 0.30928982527
## 753 bad -0.4823718117 -1.4551437931 -0.349970788 -0.10546952629
## 754 bad -0.2675412924 -0.8562008690 -1.334427175 0.30928982527
## 755 good 3.5828218360 -0.5244432756 -0.237600436 -6.24194275562
## 756 good -2.5882975604 -0.6439361365 -0.811901644 0.59495391789
## 757 good -3.5691862362 -0.0937517701 -0.999086477 0.26637613962
## 758 bad -1.2300729390 -0.3530019080 -2.111773235 0.05221841152
## 759 bad -1.2300729390 -0.3530019080 -2.111773235 0.05221841152
## 760 bad 0.7072286790 -2.0701624198 0.178794472 0.56946090168
## 761 bad 0.5070425191 -1.1300528681 -0.562739425 0.63359812541
## 762 bad 0.1443651950 0.2412699247 -1.910030916 0.48481228903
## 763 good -0.9823951857 0.4228809579 -1.860486967 0.24719264750
## 764 bad 0.1443651950 0.2412699247 -1.910030916 0.48481228903
## 765 good 0.0936464036 -0.7177111718 -1.821190071 0.38089433002
## 766 good 0.1080593870 -0.8566673045 -1.733378278 0.51647093944
## 767 bad 0.4711701416 -0.9646196690 -0.993059253 0.40648577971
## 768 bad 0.1243047088 -1.6304553860 -0.587533852 0.01004931395
## 769 good -1.7064929338 -1.9135421902 -0.151010494 0.21096782737
## 770 bad -1.4126382690 0.0008949677 -1.819151025 -0.01984992529
## 771 good -1.7064929338 -1.9135421902 -0.151010494 0.21096782737
## 772 bad 0.3363144163 -2.6820147590 -0.077956099 0.57129874296
## 773 bad 0.5643953017 -2.4913586412 0.211467452 0.61548957326
## 774 good 1.0051199931 0.5368754584 -0.529479123 -2.14120974459
## 775 good 0.9771608383 0.4793206846 -0.473836826 -2.10194937961
## 776 bad -2.2290091065 -0.2713315856 -2.407211734 -0.10269436538
## 777 good -0.6615625670 -0.4885278176 -1.337723051 -2.48958908812
## 778 good -0.5629150936 0.2046488189 -1.305793473 -2.40683392365
## 779 bad 0.2880646630 0.5584465664 -0.355384519 0.62117085572
## 780 bad -1.3426486215 -1.6026905833 0.067220596 0.17924683820
## 781 good -0.6217883321 -0.7477605473 -1.309216717 -1.84168003570
## 782 bad -1.4268600629 0.0176724598 -0.559439683 -0.69768266743
## 783 bad -0.7527717281 -2.2413511805 -0.780015753 0.51782632232
## 784 bad -1.4268600629 0.0176724598 -0.559439683 -0.69768266743
## 785 bad -1.6451886176 -1.5449462515 -0.251512549 0.12275966857
## 786 bad 1.4574000102 -0.0154235685 -0.529099940 0.87860628606
## 787 bad 1.4574000102 -0.0154235685 -0.529099940 0.87860628606
## 788 good 1.0388595499 -0.6522986841 -0.780788467 0.74648337718
## 789 good 1.0388595499 -0.6522986841 -0.780788467 0.74648337718
## 790 bad 0.6379601043 -2.4141263501 -0.508608241 0.41123722832
## 791 good 0.4544341965 -0.7054486251 0.770013337 0.03027219891
## 792 bad 0.2440909587 -2.6716407995 -0.060803150 0.66321138330
## 793 good -1.3965051923 -1.4975195410 -0.452908057 0.00440821123
## 794 bad -1.4997217522 0.9001821541 -0.997808321 0.51050147322
## 795 good 1.0696326891 2.5335376540 1.226462355 1.17288055343
## 796 bad 2.4479889198 -0.4155044579 -0.921934810 -2.08410353607
## 797 bad 1.3981878268 -1.0166888133 0.181383345 -0.70705107744
## 798 good 0.6149311533 0.9560846700 1.387956917 0.14692008043
## 799 good 0.8463407705 0.9024525317 -1.063237462 1.12226500153
## 800 good 0.8463407705 0.9024525317 -1.063237462 1.12226500153
## 801 bad -0.7433761582 -2.5324055682 0.113381459 0.64033629932
## 802 bad -0.2952003988 0.3810988361 -1.501507952 1.08071451687
## 803 good -3.3005506786 0.6604814788 2.480730498 -1.13864599101
## 804 good -0.1836637810 -0.5518320360 -0.858212977 -0.63734600629
## 805 good 0.0231833536 0.8171483602 -1.205030862 0.23763811782
## 806 good -0.4695237098 3.0903905686 1.705020989 0.36601185250
## 807 good -0.2087256144 3.1121268551 1.554360895 0.41085095087
## 808 good -0.4695237098 3.0903905686 1.705020989 0.36601185250
## 809 bad 0.3007698951 0.4496874087 -1.424375403 -2.30800383379
## 810 good 0.0764251040 1.1032459848 -0.823126067 0.52796389311
## 811 bad -1.1890236274 1.1808160188 -0.882495174 0.53651324015
## 812 good 3.2756178513 1.1436226511 -0.889148528 1.24765954668
## 813 bad 1.5053947146 0.2119901667 0.121237516 0.22574355485
## 814 bad -1.2836164505 1.9969693703 0.208700235 -0.37920598180
## 815 good 2.9763061398 0.5217929496 0.428573344 0.59404870740
## 816 bad 1.5053947146 0.2119901667 0.121237516 0.22574355485
## 817 good 0.7583820815 0.4322052084 -1.005160609 0.80002063015
## 818 good 0.9937439682 1.7273558613 1.474436749 0.10131194207
## 819 bad -1.5672663308 -1.1116655687 -1.208885557 0.37038529824
## 820 bad 0.5372440416 -0.9806748782 -1.777575823 0.58719806684
## 821 bad -1.4655912406 0.1207330714 -1.505046464 -0.69700703591
## 822 good -4.1758506384 1.9588240856 2.991435870 -0.40298045507
## 823 bad -1.6118208200 -0.1783735766 -0.536270414 -0.22519980831
## 824 bad -1.6118208200 -0.1783735766 -0.536270414 -0.22519980831
## 825 bad -0.2592232220 0.9472335724 -0.734471817 0.54279506116
## 826 bad -1.1079324675 0.2409121396 -0.036411596 0.56600754700
## 827 good -0.5875044985 2.2155612698 0.537413837 0.38613054683
## 828 bad -1.1079324675 0.2409121396 -0.036411596 0.56600754700
## 829 good -1.9100280725 0.1692155050 2.126351369 -0.07166981884
## 830 good -2.6245202017 0.7196741574 0.630618382 -0.89515607419
## 831 bad -1.5349910966 1.0315132635 -1.205585147 0.43123916762
## 832 good -2.6245202017 0.7196741574 0.630618382 -0.89515607419
## 833 bad 1.6253595491 -0.8564497007 0.670751449 -1.20693102343
## 834 bad 1.9932304384 -0.9914373361 0.642298038 -1.04044184887
## 835 bad -0.3583699613 -0.0764855431 -1.530523244 0.22190216768
## 836 bad -1.3870102749 -1.2343217090 -0.672350168 0.19581707468
## 837 good -2.1434101500 -0.2045774515 3.436656638 1.52288938678
## 838 good -2.1434101500 -0.2045774515 3.436656638 1.52288938678
## 839 good 1.1958045738 1.7126988929 0.617976188 -0.23034234338
## 840 bad -1.8895107902 0.0949311805 -0.792478218 -0.03401491890
## 841 good 1.7265331598 1.5964705345 0.716457205 0.40169145622
## 842 bad -1.6786061161 -0.6830183821 -0.471339222 -0.61969717941
## 843 good 1.5435149849 -0.8978820297 1.246819741 -0.06358917210
## 844 bad -0.3123181549 -0.9281163070 -0.534439915 -0.36408163641
## 845 good 1.4467584987 0.7090744073 1.643553309 -0.03556822813
## 846 bad -1.6376925175 -0.0740518645 -0.683526802 -0.51271389464
## 847 bad -1.6376925175 -0.0740518645 -0.683526802 -0.51271389464
## 848 good -1.0578238539 -0.1930593184 -0.982472297 -0.42215325196
## 849 bad -1.6376925175 -0.0740518645 -0.683526802 -0.51271389464
## 850 bad -1.6135692582 0.0643602704 -0.746718921 -0.55454931218
## 851 bad 0.8383283727 0.8379497326 -0.838069136 0.32871247586
## 852 bad 0.8383283727 0.8379497326 -0.838069136 0.32871247586
## 853 bad 1.3424325811 -1.9134377887 1.619726377 -1.06443584307
## 854 good 0.1138528986 -0.5347678774 1.981316075 0.00051113266
## 855 good 0.1138528986 -0.5347678774 1.981316075 0.00051113266
## 856 good -2.0694525691 0.9852256299 -0.335532851 -0.26792547746
## 857 good 0.1138528986 -0.5347678774 1.981316075 0.00051113266
## 858 good 0.0781816742 0.9358305107 1.569284500 -0.04179742394
## 859 good 1.9813340287 1.2722557034 0.554042268 0.86593129592
## 860 good -1.3404938162 1.0377475575 0.750978359 -0.92091491284
## 861 bad -1.4262102968 -1.4568629804 -0.693688929 0.30228846000
## 862 good -2.5204988910 -0.2613545497 0.951888912 0.22456889272
## 863 bad -0.2362628364 0.9965797819 -0.446449673 0.89367392637
## 864 bad -1.4856381662 -1.4320351597 -0.565823398 0.29795501226
## 865 bad -1.4262102968 -1.4568629804 -0.693688929 0.30228846000
## 866 bad -1.4525606224 -1.4492124898 -0.590611198 0.30330325218
## 867 good -1.2267239901 1.1943902012 0.951221898 -0.74449277611
## 868 good -1.1761199099 1.2336034450 0.831498829 -0.87583479514
## 869 good -1.3404938162 1.0377475575 0.750978359 -0.92091491284
## 870 good -1.6876496610 -0.4661713484 0.300433777 -0.20486704327
## 871 good -2.4403725399 -0.2031519878 0.901196840 0.17080119468
## 872 bad -2.2862066951 -0.8781497911 0.762386145 -0.29930772267
## 873 bad -0.9810409090 -0.3318164813 0.834160061 0.37778713318
## 874 good 0.4863840013 2.3597529973 0.435703568 0.46166001867
## 875 good 1.6310899129 2.1738407065 0.172787592 0.15693317399
## 876 good 0.7840480431 1.9138762619 0.920730489 -0.41943722314
## 877 bad -1.7760809302 1.2746217322 -0.367385902 0.04829131092
## 878 good -2.4403725399 -0.2031519878 0.901196840 0.17080119468
## 879 good 0.2933908880 -1.8682803881 -0.131580117 0.93753331739
## 880 bad -1.5251891591 -1.5966432424 -0.502132122 0.24693540657
## 881 bad -0.0843722099 0.7782050392 -1.185807647 0.29068910018
## 882 good -2.1788399203 -0.3730422038 0.459067230 -0.07000281628
## 883 good 0.8638678490 1.1314052159 1.232208095 -1.62011201050
## 884 bad -1.5251891591 -1.5966432424 -0.502132122 0.24693540657
## 885 good 0.2933908880 -1.8682803881 -0.131580117 0.93753331739
## 886 bad -0.2467583576 0.0661944690 -1.455934453 0.09633022778
## 887 good -0.2762469851 0.0442030157 -1.728036643 0.05596105490
## 888 good 1.0736712984 0.2591355888 1.737564309 0.41602500439
## 889 good -2.1907976053 -0.9294631078 1.086830459 0.20588041371
## 890 bad 2.5781750541 -3.6451797268 -1.567050511 1.35535815267
## 891 bad -0.9635339420 -0.4581693271 1.093712368 0.20663551332
## 892 bad -1.6319396954 -1.4520545072 -0.659904230 0.08086741664
## 893 good 0.9900930101 0.5945735588 -1.258515767 0.09383994032
## 894 bad -1.6319396954 -1.4520545072 -0.659904230 0.08086741664
## 895 good -1.5874385541 -1.4407289272 -0.477864679 0.02642694993
## 896 good -1.8579646941 -0.5602128053 0.288692690 -0.03631931461
## 897 good -0.0358885314 1.5502840311 2.232788268 -0.09834406295
## 898 good -1.8579646941 -0.5602128053 0.288692690 -0.03631931461
## 899 good -0.0358885314 1.5502840311 2.232788268 -0.09834406295
## 900 bad -1.5030461838 -0.1133984339 -2.575366620 0.69414358306
## 901 bad -0.0445460132 1.5871871595 1.265001659 0.59053825282
## 902 good -1.4960116480 -0.0227806662 -0.325655861 -0.17988252359
## 903 good -1.4960116480 -0.0227806662 -0.325655861 -0.17988252359
## 904 good -1.3139119145 -0.0207668769 -0.293336175 1.15646257487
## 905 good -1.3139119145 -0.0207668769 -0.293336175 1.15646257487
## 906 bad 0.5730783709 -1.7928418398 -0.374938077 0.57556750491
## 907 bad -0.5902039533 -0.1870003251 0.641623914 -0.20363366645
## 908 good -2.8613085572 1.4169779553 -0.299360764 -0.13331374240
## 909 good -1.4755169741 -0.9432478486 1.168611694 0.14471261959
## 910 good -0.9405889574 2.5537386436 0.883949243 0.24121587137
## 911 good 0.9249772244 2.4259589825 0.913916392 0.40450121944
## 912 good 2.5187721457 0.4133566374 -0.024634898 1.97880111769
## 913 good 0.6159549852 2.2279598586 0.196296187 0.97163435787
## 914 good 1.0191453893 1.9508126147 0.543683624 0.86743622744
## 915 good -0.9405889574 2.5537386436 0.883949243 0.24121587137
## 916 good 0.2477249174 2.6107970387 0.690659887 0.09697737254
## 917 bad -2.6185074429 0.1249727248 0.007048468 -1.87827340172
## 918 good 0.5453972996 -1.8630591132 0.893396892 1.77432583988
## 919 good 0.2524504255 -0.7020110190 1.859819268 0.04067381329
## 920 good -1.0499103951 -0.3824393659 1.782120390 -0.72813295574
## 921 bad 1.0335398033 0.9404858789 -0.522830206 0.52789508625
## 922 good 0.2524504255 -0.7020110190 1.859819268 0.04067381329
## 923 good -1.0499103951 -0.3824393659 1.782120390 -0.72813295574
## 924 good 0.5453972996 -1.8630591132 0.893396892 1.77432583988
## 925 bad 0.1363671106 1.1808653082 0.727057503 -0.09055657466
## 926 good 0.0653092792 -1.2578484087 3.475295299 -0.12488539432
## 927 good 0.7298473824 -1.7190692696 2.654780756 0.07848568237
## 928 bad 0.1439260476 -1.0122948779 -1.222861120 -0.10808094840
## 929 bad 0.1363671106 1.1808653082 0.727057503 -0.09055657466
## 930 good 0.1331766935 1.9505291458 1.379804057 0.43204704436
## 931 bad -2.1339230114 0.4054544923 -0.802126866 -0.63740981984
## 932 bad -1.3733506754 -0.2794791923 -1.031355580 -0.20963199074
## 933 good -0.4469897279 -1.0634279740 0.565108210 0.16119402900
## 934 bad -1.3733506754 -0.2794791923 -1.031355580 -0.20963199074
## 935 bad -2.1339230114 0.4054544923 -0.802126866 -0.63740981984
## 936 good -0.0006385448 0.6745079478 2.064523523 0.41081272952
## 937 good -0.0006385448 0.6745079478 2.064523523 0.41081272952
## 938 bad 2.1181114655 1.2111830893 -1.297255165 0.69102183005
## 939 good -0.8709115498 1.2836738698 2.670852258 0.12059973927
## 940 bad -2.5480099542 1.7912289914 0.244030691 -0.13599884169
## 941 good 0.5648128008 2.1414948996 1.743781167 0.16780762667
## 942 good 1.0679246591 2.0200721597 1.409111386 1.58778286872
## 943 good 1.5899546566 0.0325031937 -0.327925141 0.43195833965
## 944 good 1.2334433723 0.0237350708 -1.018236028 0.33341583340
## 945 good 0.5046874206 1.7203150962 1.521470322 0.52776671438
## 946 good 1.2571827796 1.8365027777 0.288703419 -0.08222504978
## 947 good 2.0792416335 1.3666387400 1.260224546 -0.11574114107
## 948 good 0.1329178189 2.7311857354 1.366523385 0.02306603724
## 949 good 0.4530389182 2.4189623600 1.762744467 -0.01339412428
## 950 good 0.4530389182 2.4189623600 1.762744467 -0.01339412428
## 951 good 0.4530389182 2.4189623600 1.762744467 -0.01339412428
## 952 good 0.1329178189 2.7311857354 1.366523385 0.02306603724
## 953 good 0.0651340999 2.1597110664 0.613595732 0.27078595343
## 954 good 0.8107129267 2.8371762344 0.962035751 0.60308021250
## 955 good -0.7745710086 2.0713273176 0.609547611 0.50695223518
## 956 bad 0.8437243245 1.5777728347 0.603277964 -0.05719368124
## 957 good 0.7114707118 2.1122370052 0.313359291 -0.14159354047
## 958 good 0.8447671694 1.0306252210 0.736879963 0.43946563286
## 959 good -1.6683654481 -0.1122416297 0.848239400 -1.03796543848
## 960 bad -0.7810361720 0.0111827168 -1.102990807 -0.19020509312
## 961 good 0.2169593784 0.8459938990 0.988688965 -0.39740988412
## 962 bad -0.8188084292 0.5993369027 -1.273262901 -0.49174294333
## 963 bad -1.1410638547 0.3360613780 -1.373380424 -1.15333727825
## 964 good 0.8854650000 1.1730176500 1.241655564 -0.23493914874
## 965 good 0.2169593784 0.8459938990 0.988688965 -0.39740988412
## 966 good 0.3022742374 0.4615886547 1.829515795 -0.25111847534
## 967 good 0.8801810210 2.0858302192 0.274487508 0.22911685631
## 968 bad 0.2374852901 -2.2407305859 -0.545614547 0.27341665274
## 969 good 0.1230599105 0.7537731105 2.117616238 0.66767625740
## 970 bad -1.3964723164 0.4321049725 -0.860868542 -0.01956308413
## 971 good 1.5866049448 2.1710155150 0.494739618 -0.05452652077
## 972 good 1.5866049448 2.1710155150 0.494739618 -0.05452652077
## 973 good 1.3684382856 2.0253062611 0.280464555 0.17625818175
## 974 bad -0.2214779996 1.8728698913 0.159003092 0.06019466731
## 975 good 0.7830179587 1.4749517259 0.916272874 1.52919899818
## 976 bad -0.6043311386 -1.5880934099 0.744245955 0.32802991072
## 977 bad -0.6043311386 -1.5880934099 0.744245955 0.32802991072
## 978 bad 0.7243532532 -2.8171898640 0.020630940 0.31663970201
## 979 good -0.8696541856 1.2711624875 0.713339819 0.91763894460
## 980 bad 2.0654286326 2.1024456553 -0.944911564 0.51316113230
## 981 good 0.4130091449 1.1216706667 -0.618529342 0.14474786719
## 982 bad -0.0026164093 -0.0381765143 -1.599895345 0.03277072869
## 983 good -1.0392038117 -0.7569417140 3.660334511 -0.28748860917
## 984 good 0.4130091449 1.1216706667 -0.618529342 0.14474786719
## 985 bad 2.0654286326 2.1024456553 -0.944911564 0.51316113230
## 986 good -1.9649983508 1.3118232385 -0.587988355 0.11447243321
## 987 good 0.4074557373 2.6664201717 0.134677076 0.50553190275
## 988 bad -0.6759148479 -1.3728888665 0.806913446 0.30357432897
## 989 bad -0.2863638456 0.0660783514 -0.581702521 -0.09394947762
## 990 good 0.7950246409 1.6188872424 0.388192635 0.65507795891
## 991 bad -0.2863638456 0.0660783514 -0.581702521 -0.09394947762
## 992 bad -0.6889745007 -1.2163353274 0.642865701 0.42910837885
## 993 good -1.4422301438 -0.8780978552 0.297160206 0.17718019639
## 994 bad -0.6889745007 -1.2163353274 0.642865701 0.42910837885
## 995 bad 1.9162762057 -1.1245406516 -0.215218138 1.20459290902
## 996 good -1.0788223830 -0.3992752446 -0.559597272 -0.10539080201
## 997 good -3.6508563635 1.9312630413 0.317569178 -0.60177527848
## 998 good -3.6508563635 1.9312630413 0.317569178 -0.60177527848
## 999 good -0.1852979109 0.8020227249 -2.327838926 0.41949798340
## 1000 good -3.6051215740 2.2209062480 0.560096845 -0.05342029761
## 1001 good -0.9397047159 1.9685741840 0.572052177 -0.10004921450
## 1002 good 1.0092364511 0.1002001288 1.400872628 0.03149974602
## 1003 good 0.4493997617 1.8117903229 1.450085265 -0.17603781514
## 1004 good -1.6153190699 2.9791113543 1.313152097 0.15836539934
## 1005 bad -0.1567545890 -0.1412597108 0.520895817 0.22528668020
## 1006 good -1.6153190699 2.9791113543 1.313152097 0.15836539934
## 1007 good 0.4493997617 1.8117903229 1.450085265 -0.17603781514
## 1008 good 0.4612273395 1.8944530005 1.371031361 -0.21001007944
## 1009 good 0.7755577952 1.4537558695 1.178786252 0.05605832181
## 1010 bad 1.1878309015 -0.4668145661 0.662629906 -0.04206810877
## 1011 good -0.1064233368 2.8712874361 1.284180273 0.52223744153
## 1012 good 0.5021854794 1.2577853589 0.007558821 0.23767267815
## 1013 bad -1.1993248862 -0.4392515800 -2.089643569 -0.23556459413
## 1014 good -1.5729317663 -0.5334266786 -0.927285416 -0.43157322836
## 1015 good -0.7143899278 1.6714778937 -0.684469263 -0.67164803214
## 1016 good 1.8569440847 1.5311048261 0.932038517 -0.07202175625
## 1017 good 0.1662851728 2.0454649413 1.600839671 0.12039457965
## 1018 good -0.5158032245 0.5789512031 4.102811548 0.75463273388
## 1019 good -0.5158032245 0.5789512031 4.102811548 0.75463273388
## 1020 bad -1.5305735079 0.7433185191 -0.440525435 -0.20217299452
## 1021 good 2.0564703173 2.5151707918 0.161085553 0.54484583516
## 1022 good 2.0564703173 2.5151707918 0.161085553 0.54484583516
## 1023 bad -1.4018563833 1.3113050677 -0.902129640 0.22560052025
## 1024 good 0.0474004970 2.5881094060 0.719828517 0.24112418284
## 1025 good -1.3434856892 0.6389697515 -0.831478869 -0.23067755399
## 1026 good -0.7400140317 -0.9309988213 -1.458176314 0.07973834934
## 1027 good -1.1786805220 1.9574386330 1.850261221 0.46059489868
## 1028 bad -3.1673972353 -0.5429390795 0.596722919 0.11725773541
## 1029 good -0.8291878973 -1.4258759757 1.582671684 0.47011403161
## 1030 good -1.3434856892 0.6389697515 -0.831478869 -0.23067755399
## 1031 good -2.0693199544 1.1817838582 -0.365769101 -0.22248898314
## 1032 good -1.6079546499 1.0942945411 -0.527500780 -0.41347684209
## 1033 bad -0.7452891991 -0.4533480784 -2.596274701 0.44759541159
## 1034 good -0.9721179817 0.2262213282 -0.526185901 -0.09621299407
## 1035 good -0.1323453435 -0.6891406885 -1.488748533 0.66485711962
## 1036 good 1.2536645651 1.5458886315 -0.387605304 0.30552347791
## 1037 good -0.5467211896 1.2045778999 2.338312780 0.28922960262
## 1038 bad -1.4741802592 -1.4306871183 -1.545314576 -0.17300635702
## 1039 good 0.7246510092 0.2617912946 2.432435468 1.04471450483
## 1040 good 0.1562830487 0.2345371818 0.624993309 -0.48408379307
## 1041 bad -2.0820949410 -0.8232595212 -1.651619150 -0.51864352482
## 1042 good -1.0940532504 0.5140917900 -0.316237409 -0.46777690551
## 1043 good 0.1562830487 0.2345371818 0.624993309 -0.48408379307
## 1044 good 2.0225003397 -0.7703617516 1.062906589 1.94854814319
## 1045 good -1.4851746237 1.3009791581 2.021237226 0.58757940950
## 1046 good -1.9222880010 0.0924467438 1.252098154 -0.10723958175
## 1047 good -1.5942850527 -1.0919285267 -0.092478416 -0.20855717809
## 1048 bad -1.2060379166 -0.4405967805 0.766591376 -0.20264140503
## 1049 good 0.7409166582 1.7339008966 -0.411227626 -0.35275107482
## 1050 good 0.7437478254 1.4496546848 -0.347051675 -0.33466170485
## 1051 bad -1.2060379166 -0.4405967805 0.766591376 -0.20264140503
## 1052 bad 4.2162276498 -0.7377005138 0.244240605 -7.07811069437
## 1053 bad -3.4968403938 1.2057335852 1.423567812 -0.06795586790
## 1054 good -0.2841906742 2.4700832927 1.821408479 -0.05760438142
## 1055 good 0.6040498059 -0.6644878454 -1.320779917 -0.29318238224
## 1056 good 0.6040498059 -0.6644878454 -1.320779917 -0.29318238224
## 1057 good 0.8997341230 1.9577636015 0.340178889 0.63537791403
## 1058 bad 0.7972954214 -2.1873602251 0.297636475 0.39811573479
## 1059 good 1.5575547700 -0.0393064729 1.441081158 0.11259191131
## 1060 good 0.8997341230 1.9577636015 0.340178889 0.63537791403
## 1061 good 2.7951299384 2.0411948251 -0.518917749 0.51205969627
## 1062 good 0.4593969268 2.6091355016 1.178875890 0.15693304602
## 1063 good 0.1638407106 1.9505599006 0.425189471 -0.23553497390
## 1064 good 1.5322471535 2.4732759785 0.974828920 0.72078726725
## 1065 good -1.6056656004 1.6577098590 -0.634509916 0.08678818494
## 1066 good -0.6127640740 0.7099971949 -1.061658549 -0.07585563493
## 1067 good -2.0731462052 1.4857016101 1.236996241 -0.37762284575
## 1068 good 2.3549073368 2.4786339513 0.382090316 0.16974085980
## 1069 good 2.3549073368 2.4786339513 0.382090316 0.16974085980
## 1070 bad -0.0805716229 -0.7402475648 0.518562775 0.28611551890
## 1071 good 0.9652212690 1.3332782410 1.617820963 -0.39327683697
## 1072 bad 0.6744580243 -3.7918798090 -0.335310693 1.49610995036
## 1073 good -0.4945297161 -1.3469643229 0.936224335 0.05068889883
## 1074 good -0.1035886412 0.0361726664 -0.302145283 0.29451838306
## 1075 bad 0.6744580243 -3.7918798090 -0.335310693 1.49610995036
## 1076 good 0.6821935466 -1.2581999435 2.229429162 -0.10418234676
## 1077 good 1.4669131406 2.6432359510 0.592141357 0.13067686258
## 1078 bad 1.8161800514 1.1493379196 0.083022693 1.74439398468
## 1079 bad 1.8161800514 1.1493379196 0.083022693 1.74439398468
## 1080 good 1.3705473838 -4.3177149806 5.863529534 2.95653057057
## 1081 good 0.9483637811 3.1167123534 0.628288408 1.11768097822
## 1082 good 1.3784306740 -4.5081480178 5.971343018 2.96809295136
## 1083 good -0.5130708359 -1.1504881105 0.813446284 0.15434924329
## 1084 good 0.3640339410 0.0627820452 2.260490530 0.19766050854
## 1085 good -0.5130708359 -1.1504881105 0.813446284 0.15434924329
## 1086 bad -0.9418345631 -1.6218041703 0.676761763 0.16966227351
## 1087 good 0.5178170446 1.7117796533 0.377175838 1.28640189705
## 1088 good -0.1192679699 1.6954216862 1.860878306 0.06004021994
## 1089 good 2.7752052933 0.3404560344 0.220350419 -0.10962444209
## 1090 good 2.7752052933 0.3404560344 0.220350419 -0.10962444209
## 1091 good 1.5977745444 -0.1105252366 3.027598236 0.51581871964
## 1092 good -0.1350369366 1.8473437219 0.638114763 0.08786431017
## 1093 good -1.8640150455 1.3089348677 0.036040924 -0.19363569250
## 1094 good 0.6250735791 1.7480564214 1.380145693 -0.28772289266
## 1095 good -1.1667348255 -0.2475357504 -1.071918494 0.49026402188
## 1096 bad 1.3833459029 1.2513269875 -0.639904972 0.81623372178
## 1097 good -1.1667348255 -0.2475357504 -1.071918494 0.49026402188
## 1098 bad 0.5048807336 0.9293799456 -1.389862191 -0.06483489460
## 1099 good 0.9676095272 2.3806330537 1.438404782 -2.43787691536
## 1100 bad 0.5048807336 0.9293799456 -1.389862191 -0.06483489460
## 1101 good 0.4238493295 1.4763366839 2.345481940 -0.15988110639
## 1102 good -1.2578790406 1.5174636468 1.053369383 -0.01012206840
## 1103 good -2.3571250047 0.7507524542 0.630482316 -0.21731948750
## 1104 good -1.2578790406 1.5174636468 1.053369383 -0.01012206840
## 1105 good -0.7561956706 1.6909548099 1.457528012 0.02916967102
## 1106 bad -2.0606272685 1.3365452165 1.534090745 0.33797605253
## 1107 good -0.1361446284 2.6229091333 1.493176192 0.52974839030
## 1108 good 0.6526547323 2.0450397719 1.364659565 -0.27324733018
## 1109 bad -0.6013034462 -0.1238308388 -2.583959077 0.47949222018
## 1110 good 2.2705329537 -0.7068704844 0.637030910 -0.97100023767
## 1111 good -0.8178192052 1.0523079930 -1.199336406 -0.30965013339
## 1112 good -2.7170083534 0.4795260035 2.146338385 -0.40852378474
## 1113 good -0.2578831168 1.9346281540 1.000386214 -0.15437536393
## 1114 good 1.2823604321 1.6883475750 -0.732733008 0.23498740661
## 1115 good -2.4577856304 0.6838292469 4.562744148 0.61176021541
## 1116 good -1.6558545214 0.4198629735 -0.352829846 -0.22188153600
## 1117 good -1.6558545214 0.4198629735 -0.352829846 -0.22188153600
## 1118 good -1.6558545214 0.4198629735 -0.352829846 -0.22188153600
## 1119 good -2.0180357789 2.1906997608 1.780292409 0.36419706427
## 1120 bad -3.5235965067 1.8581979104 0.693395874 -0.14785458229
## 1121 good -0.8207138803 2.4815686560 1.526529854 -0.24759622657
## 1122 good -2.7099348979 1.0631662335 1.166199579 0.11355403146
## 1123 good -3.2496795281 0.9556723194 1.668039093 0.21645853209
## 1124 good 1.4103971729 1.1041808390 0.375728854 0.42987356631
## 1125 bad -2.4949080705 1.3573492873 -0.490167713 -0.52635184793
## 1126 good 0.1764756591 1.9336734446 1.196551585 0.48615429472
## 1127 good -2.4149244741 3.4164481129 2.013538220 0.05535553761
## 1128 good -2.9672855315 -0.5750748681 0.739746771 -0.07285800803
## 1129 bad 1.4636996652 -1.4326953860 0.627615424 0.35108740810
## 1130 good 1.6023043619 -0.7305845441 0.624960360 0.57541827178
## 1131 good -0.1752010446 0.9819360487 -1.635989102 0.29897220992
## 1132 bad -1.6709102997 -2.9089924216 3.356395990 0.74339618342
## 1133 good -0.9540611058 2.1011528307 2.963797563 -0.71179361500
## 1134 good -0.6145483318 0.3230935921 0.028746726 0.52651374666
## 1135 good 0.0612159246 2.4323519940 0.996180354 -0.00375636158
## 1136 good -0.4246519111 1.1796118868 2.149931341 0.40383836357
## 1137 good 1.4508338527 1.7328823822 -0.277416609 0.10835917662
## 1138 good 1.4508338527 1.7328823822 -0.277416609 0.10835917662
## 1139 bad 0.5701224680 -2.3358499512 0.282553162 0.27315944476
## 1140 good 0.0274242293 -1.0145932610 -0.202160480 0.03536682195
## 1141 good -0.3660102601 -1.4240359379 0.991665591 -0.13963424001
## 1142 good 0.4560924793 -0.2189764806 1.672982393 -0.38076241243
## 1143 good -1.6395363665 1.9017475332 0.424707623 0.13858544369
## 1144 good -0.6665709969 0.9496938094 0.835751054 -0.50072209536
## 1145 bad -0.8175597534 -1.0337299083 1.578175437 0.29524831705
## 1146 good 0.2696369206 -0.1387448843 2.038032821 -0.07984857345
## 1147 good 0.1810606054 0.5905481030 -0.767196339 -2.49221330068
## 1148 good 1.3284913183 1.2515737527 0.213477153 2.19956451530
## 1149 good -0.3225616374 1.7892876415 0.818561845 -0.13067863358
## 1150 good 0.6934025275 2.4762212719 0.432863835 1.07981670974
## 1151 good -0.1997322016 2.6620823497 1.378102895 0.11934153638
## 1152 good -2.3056415062 0.6214994824 1.835317949 0.26774473917
## 1153 bad 0.1859398140 0.1505130824 -1.097014957 -0.31541765534
## 1154 good 0.6667852317 0.8129297883 0.584273209 0.21251993451
## 1155 good -2.4208052850 -1.8276232712 1.978514490 -0.54081221928
## 1156 bad 0.1859398140 0.1505130824 -1.097014957 -0.31541765534
## 1157 good 0.4864959579 -0.5059938997 3.785509220 0.15204316682
## 1158 good -2.6024972836 0.5056614440 3.041197402 -0.67118027391
## 1159 good 0.2159213758 0.0542252316 2.103233520 -1.53493310283
## 1160 bad 1.6583614445 0.9017255044 0.030287932 -0.02369316121
## 1161 good 2.0700022370 1.8908351454 0.280890617 0.24483620267
## 1162 good 0.6260656600 1.2578573066 0.125497938 -0.46008969019
## 1163 good 0.5049000296 2.0135152768 1.334273270 0.00715623135
## 1164 bad -0.1945416101 0.3739696219 -1.471076410 -0.24682468138
## 1165 bad -0.1945416101 0.3739696219 -1.471076410 -0.24682468138
## 1166 bad 3.4251623670 -0.4574405881 0.006293072 -5.60880748057
## 1167 bad 2.1034272622 0.2686030373 -0.239925861 -1.34860899663
## 1168 good 0.1796100458 0.7145238433 2.735806935 -0.27312801450
## 1169 good -0.9331316032 1.5293376187 1.331785451 0.39438728107
## 1170 good -0.6929864871 1.7537279638 0.092928531 -0.10862482321
## 1171 good 0.7444073910 1.8041025527 -0.331265039 0.20949874896
## 1172 good -1.8229420646 -0.4175849537 0.501616320 -0.38104666865
## 1173 good 0.8719045769 2.3207669659 0.651538844 0.22841387403
## 1174 good -0.3254967501 -0.7875874214 0.583452259 0.01471024130
## 1175 good -0.3254967501 -0.7875874214 0.583452259 0.01471024130
## 1176 good -2.5524390270 -1.6533480273 1.888990274 -0.56479980221
## 1177 bad -2.5873923623 -1.4052212582 -0.149291892 0.98065326087
## 1178 good -2.4263034856 2.0109870516 0.396007475 -0.01643997224
## 1179 bad -3.8985619000 -0.5878193478 0.582344255 -0.47667100733
## 1180 good 0.4095080107 0.9425929075 0.972013806 -0.30573509003
## 1181 good 0.4095080107 0.9425929075 0.972013806 -0.30573509003
## 1182 bad 0.3546933433 2.3689997597 0.030249540 0.84999520123
## 1183 good 1.0841936754 -0.9076611722 1.735697984 0.16261059645
## 1184 bad -1.3667007237 -0.7694693846 -0.772416882 -0.36105011901
## 1185 bad -1.1283392301 -0.8659873905 0.820793555 -0.44063885404
## 1186 good -1.3926637112 1.7121928862 0.786779660 0.23431211616
## 1187 bad -2.0053672700 0.0332056516 -0.521178859 1.67006895770
## 1188 good -1.3926637112 1.7121928862 0.786779660 0.23431211616
## 1189 bad -1.1283392301 -0.8659873905 0.820793555 -0.44063885404
## 1190 bad -1.3915018576 0.1201695600 -2.744447552 0.25194639368
## 1191 good 0.9902707793 1.8101181631 0.933074480 1.36582667343
## 1192 bad -2.5117574729 0.2515689978 -1.777031453 -1.34359201918
## 1193 good -0.9039994711 1.8815745436 2.300858555 -0.12185505507
## 1194 bad -2.5398612722 0.2452222646 -1.769949618 -1.35678011037
## 1195 good -0.8832304488 -0.9746657479 -1.005704142 -0.85420113915
## 1196 good -1.3826877721 -0.5732240476 0.251034872 -0.23743038726
## 1197 good -0.0549877257 -1.5906482747 0.016195403 0.23987237558
## 1198 good -0.6799679824 1.3614049080 -1.167189529 -0.11875988539
## 1199 good 0.2028474917 0.3097376664 1.826127269 -0.09344458467
## 1200 good -0.0549877257 -1.5906482747 0.016195403 0.23987237558
## 1201 good -0.6799679824 1.3614049080 -1.167189529 -0.11875988539
## 1202 good -0.4825220856 2.2639307777 0.398257186 0.36493982344
## 1203 good -0.1820473112 2.2259402450 0.556476266 0.03306531005
## 1204 bad 1.2404204924 -2.9828741204 -0.625043818 1.13131146273
## 1205 good -0.1107334743 0.5265772044 1.893901006 -0.57598496171
## 1206 good -0.1107334743 0.5265772044 1.893901006 -0.57598496171
## 1207 good -0.1107334743 0.5265772044 1.893901006 -0.57598496171
## 1208 bad 1.4603896002 -0.6510854628 0.131625656 -1.52371352264
## 1209 good -0.1107334743 0.5265772044 1.893901006 -0.57598496171
## 1210 good -0.8634575564 1.4655412217 1.575949235 -0.91879282541
## 1211 good -1.8034550429 0.7664347497 -0.891892217 -0.44553625547
## 1212 bad -0.9477134204 -0.6478821337 0.131904782 -0.26006909721
## 1213 good -1.8034550429 0.7664347497 -0.891892217 -0.44553625547
## 1214 good 1.3328778439 1.7874007303 -0.170641535 -0.02958405298
## 1215 good 1.4028652574 1.8665970199 -0.509400128 0.78241700849
## 1216 good 0.4104447487 1.2638054087 1.338825163 0.27593403196
## 1217 good 0.0769227568 -0.5345226115 -0.340592475 -0.27860676078
## 1218 good -0.1034668608 -0.2926704958 3.394542341 -0.04963357797
## 1219 good 0.2222529396 1.3018488622 0.037654727 -0.24542102474
## 1220 good 0.4277499738 0.6811122921 1.667555147 -0.15585603158
## 1221 good 1.9718987973 1.0098668020 1.068449208 -0.47606819498
## 1222 good 1.9718987973 1.0098668020 1.068449208 -0.47606819498
## 1223 good -0.3368289821 -1.8830039520 0.344492144 0.66111443558
## 1224 good 1.3531456794 2.0456538400 0.902684620 0.30435661735
## 1225 good 3.3222142256 1.2279174572 -0.577139213 0.43021641689
## 1226 bad 1.1491837228 -1.2303869287 -0.759833551 0.72530866088
## 1227 bad -0.2700639859 -1.5057163607 -0.601712905 0.72260373328
## 1228 bad 0.5899207062 0.4267348454 -1.295537049 -0.58702513145
## 1229 good -4.0230494742 0.9978635048 3.286287582 -0.35395547928
## 1230 bad -0.2147655604 -0.6443239693 0.149540812 0.04536500227
## 1231 good -0.1829115511 1.2049767814 2.333424146 0.02884539152
## 1232 bad -1.6868680167 -2.7860164937 0.996435215 0.20397449803
## 1233 bad -0.2147655604 -0.6443239693 0.149540812 0.04536500227
## 1234 bad 0.9690916671 0.7226752979 -0.103469008 1.10216389348
## 1235 good -3.1798981951 1.6014499943 0.194021898 0.61129434948
## 1236 bad 0.2943574083 -1.8703423173 2.273673121 3.39065307840
## 1237 good -0.7816017377 0.5313936190 -1.502569123 -0.30529617771
## 1238 good -3.1798981951 1.6014499943 0.194021898 0.61129434948
## 1239 bad -1.1457717714 -0.4262377383 -2.080056080 0.47825026051
## 1240 bad -2.3752546738 0.6108382303 0.001924781 0.74332066410
## 1241 bad -0.7367160859 -1.4893003594 0.007009627 0.40321197830
## 1242 bad 1.3503361782 -0.7545306678 0.587626696 0.68945296905
## 1243 good -0.0573184075 1.6570040574 1.487592641 0.61642628496
## 1244 bad 0.0414066576 -0.9800598657 -0.722778926 0.18343278919
## 1245 good 0.3946813448 -6.6862945201 4.651730221 3.70478220360
## 1246 bad -1.1140315717 0.3123637532 -0.117701539 -0.37476836314
## 1247 bad -1.1381319122 -0.3285911531 -1.034743125 -0.13371354224
## 1248 bad -1.1140315717 0.3123637532 -0.117701539 -0.37476836314
## 1249 good -0.8024693463 0.5929464836 1.425461845 -0.48395908993
## 1250 good -1.8665899419 -0.1967103957 0.278947991 -0.15370859832
## 1251 good -1.8665899419 -0.1967103957 0.278947991 -0.15370859832
## 1252 bad -0.7593321936 -1.1005976991 0.032026058 0.04258139402
## 1253 bad -1.6893591896 0.3039675005 -1.841226578 -0.98772448019
## 1254 bad -1.5813226087 0.5902355427 -1.768629517 -0.66891042443
## 1255 bad -1.3597103372 -0.0820221676 -0.245714362 -0.36420337850
## 1256 bad -2.9778562236 -0.3648338151 0.544963775 0.27554439255
## 1257 bad -0.5994819652 -1.8628567310 0.343829686 0.31899399298
## 1258 good -0.2800348676 -0.5157085895 0.605263384 0.33881432179
## 1259 good -1.8370623408 0.1285106671 -0.096631034 -0.76966228547
## 1260 good -1.8370623408 0.1285106671 -0.096631034 -0.76966228547
## 1261 bad 3.8135716975 -1.2290241076 0.041239755 -6.25591664885
## 1262 bad -3.4918466765 -0.1036422894 -0.932834358 -0.46438272131
## 1263 bad 1.2167260124 -1.2014117854 0.611329819 0.67729323519
## 1264 bad -1.1906258905 -0.5249447902 -2.160899535 0.26620742885
## 1265 good -0.5388651812 0.7068268857 2.022355088 0.02516650220
## 1266 good -1.4563879281 0.0100110627 -0.324826055 -0.16419671378
## 1267 good -1.4563879281 0.0100110627 -0.324826055 -0.16419671378
## 1268 good 1.7808295455 1.6872589859 0.849955336 -0.06825392760
## 1269 good -0.7797767438 -0.4353595457 0.161128108 0.12749462729
## 1270 good -3.5313343973 0.7334976333 3.915455435 -0.60150364231
## 1271 good -4.2480288975 1.4175565209 3.785988754 -0.53720129933
## 1272 good -1.7625287765 0.4566050733 1.747658819 0.35821423097
## 1273 bad -2.7907962427 0.0937878577 1.058038487 -0.14267369657
## 1274 bad -0.6407439226 -1.1098585644 -0.079436644 0.41558869987
## 1275 good -1.0982092152 0.4886973875 0.143826241 -0.19023669670
## 1276 good -0.3481681690 -1.0928382816 -0.936141510 0.28089950644
## 1277 bad 0.3197711270 1.6493237575 0.644417123 1.96418110395
## 1278 good -1.3580298383 0.5306382541 -1.960828052 -0.92113641046
## 1279 good -0.3481681690 -1.0928382816 -0.936141510 0.28089950644
## 1280 good 0.6562404087 2.6783695517 0.324849669 0.61570061595
## 1281 good -1.0022668087 -0.4710875272 0.903334497 -0.17047346415
## 1282 good -1.0022668087 -0.4710875272 0.903334497 -0.17047346415
## 1283 good -1.3337539817 0.5198388261 -1.097018181 -0.48546206808
## 1284 good 0.3124952899 -0.7872322591 -0.281706431 0.07850048576
## 1285 bad -1.3653578902 0.5870230806 0.700445788 0.20018762963
## 1286 bad 1.9613537555 0.4747452557 0.303735022 0.68874757411
## 1287 good -0.7731999967 2.0179278633 2.464437224 0.61503269392
## 1288 bad -2.0020113895 2.4776525834 0.088185000 1.08497336914
## 1289 bad 1.0388964136 -2.0547138314 1.230839913 -0.78398327101
## 1290 bad 1.0388964136 -2.0547138314 1.230839913 -0.78398327101
## 1291 bad -1.7498644627 0.9648433925 -1.189730007 -0.57583707946
## 1292 good -0.6518071239 -0.6179770457 0.454359416 -0.27002581852
## 1293 good -2.7270150849 1.5706502405 1.698225785 -0.02988058004
## 1294 bad -1.5021698897 0.3065345061 -2.207902379 -0.05410600665
## 1295 good -0.6518071239 -0.6179770457 0.454359416 -0.27002581852
## 1296 bad -1.2850885782 -3.3276033744 0.809130248 0.64387940278
## 1297 bad -1.2850885782 -3.3276033744 0.809130248 0.64387940278
## 1298 good -1.8843920755 1.3579810955 1.029951807 0.01667732895
## 1299 good -3.4463585007 1.7554389676 0.705058374 -0.41386261859
## 1300 bad -3.4338022796 -0.4075798769 -3.654173060 -0.85657420574
## 1301 good -3.8666899097 0.8792307417 0.794172378 -0.40089311932
## 1302 good -2.3816811160 -0.5265135291 0.189061473 -0.91703519392
## 1303 good 0.3590796173 2.0519444695 0.371785100 -0.13727611367
## 1304 bad -0.0721092193 0.1815546160 2.196908171 0.02902452340
## 1305 bad -0.6268346437 -1.4015092286 -1.026507114 0.20082749513
## 1306 bad -0.7128922874 -1.3120115003 0.181935686 0.25135611018
## 1307 bad 0.5788587051 -1.4563774276 -0.182515271 0.37806926475
## 1308 bad -1.8832054282 0.0696968590 -0.188010591 0.66185379372
## 1309 bad 0.5788587051 -1.4563774276 -0.182515271 0.37806926475
## 1310 bad -1.1265872415 -1.2794706365 -0.276949058 -0.34227397263
## 1311 bad -0.7128922874 -1.3120115003 0.181935686 0.25135611018
## 1312 good -2.0438281189 1.6512415803 1.392675396 0.27488437933
## 1313 bad -1.8464413894 -0.5835924994 -1.867874157 0.01524969684
## 1314 good -0.9565888478 -0.3489541456 0.681793999 0.11062280915
## 1315 good -0.9757068117 -0.6336299825 0.879239484 0.18856619986
## 1316 good -0.4402844475 -1.0860702751 -0.065735093 -0.05425070109
## 1317 good -5.2735227320 0.9071157448 1.154102961 -0.19381131521
## 1318 good 1.1925632588 1.4131835602 0.737386755 0.18312406102
## 1319 good -0.4402844475 -1.0860702751 -0.065735093 -0.05425070109
## 1320 good 4.4979236037 -1.5460551335 -0.171868868 -7.01534867437
## 1321 bad 0.8913743409 -0.8872513083 -0.482584677 0.17100605627
## 1322 good -5.3859379293 0.8817288120 1.182430302 -0.24656367995
## 1323 bad 0.1396590314 2.2052943415 0.755500567 0.60861865487
## 1324 good 0.6808666792 0.7025938086 1.602855417 -0.17999053614
## 1325 good -1.3229375613 0.5722582403 0.533874326 -0.24299591787
## 1326 good -1.3229375613 0.5722582403 0.533874326 -0.24299591787
## 1327 good -1.3229375613 0.5722582403 0.533874326 -0.24299591787
## 1328 good -1.3229375613 0.5722582403 0.533874326 -0.24299591787
## 1329 bad -1.4438833175 -0.0376149437 -0.764244830 -0.10794288934
## 1330 good -0.5557270531 -1.0791791948 -0.010112523 0.03388696556
## 1331 good -0.5557270531 -1.0791791948 -0.010112523 0.03388696556
## 1332 bad -0.4696815623 -2.4464161222 -0.838562936 0.41393488200
## 1333 good -0.1434018196 0.9540455803 -1.130436607 0.45920556479
## 1334 bad 0.1361393634 -0.5449444252 -1.701261166 0.45166036059
## 1335 bad -1.5716959834 0.1953208497 -2.317753062 -1.39361396695
## 1336 good -2.2901653658 0.8009405583 1.283663782 -0.14191986854
## 1337 bad -2.3407175525 0.2950508213 -0.682392807 0.06311730152
## 1338 bad -2.3407175525 0.2950508213 -0.682392807 0.06311730152
## 1339 bad -2.3407175525 0.2950508213 -0.682392807 0.06311730152
## 1340 good -1.5102855614 0.5302309082 -0.424282049 -0.13586303055
## 1341 good -1.5102855614 0.5302309082 -0.424282049 -0.13586303055
## 1342 good -1.5102855614 0.5302309082 -0.424282049 -0.13586303055
## 1343 good -1.4854102377 0.1845430335 -0.526359256 0.00001970743
## 1344 good -1.5102855614 0.5302309082 -0.424282049 -0.13586303055
## 1345 bad 2.2569020918 1.3670954987 -0.613046074 1.29707826631
## 1346 good -0.2338915512 0.8040973667 -0.421902729 0.58201242220
## 1347 bad -2.7638842670 1.4313404824 -0.349276391 0.07111736726
## 1348 bad -1.7228297880 0.4640498608 -1.954020822 0.24877218688
## 1349 bad -1.7228297880 0.4640498608 -1.954020822 0.24877218688
## 1350 bad -1.9709342190 -0.2574310763 0.108218808 0.20964346941
## 1351 bad -0.2422519065 -1.2902717998 0.427591032 0.49915854741
## 1352 good -2.2594679588 0.9854279266 0.211652668 0.24962495239
## 1353 bad -1.3967346976 -0.4857587715 -0.941869111 0.25577397439
## 1354 bad -1.3967346976 -0.4857587715 -0.941869111 0.25577397439
## 1355 bad -1.4991551921 0.4419435563 -1.097114312 0.06254400834
## 1356 bad -1.3112408615 1.1866758750 -0.090514849 0.03364553797
## 1357 bad -1.3463186424 1.2806047590 -0.217035553 0.04476878239
## 1358 good -1.3464121555 -0.5523678276 1.296283946 0.36918550056
## 1359 bad 0.0117604039 -4.2246015739 0.901443035 0.07328209090
## 1360 good 1.9942162503 1.2342004386 -0.945032639 0.09988882140
## 1361 bad 0.4823557454 0.5465292611 -0.414623802 0.08145817370
## 1362 bad -0.8807772879 -0.8035450588 -1.434385833 0.10305451816
## 1363 good 1.9942162503 1.2342004386 -0.945032639 0.09988882140
## 1364 bad -0.7322174119 -0.6406711101 -1.336702147 0.27056107625
## 1365 good -2.1520702102 0.9478416352 -0.109502924 -0.19791576093
## 1366 bad 0.0562929054 -0.1676759214 -1.061692251 -0.59089594419
## 1367 bad -1.2271472992 -0.2197316203 -1.409072386 -0.29659107744
## 1368 good 0.7055913370 -0.6143658972 1.154142575 -1.88705562303
## 1369 good -0.4429682148 -0.9613078223 -0.784490636 0.32858317140
## 1370 bad -2.4526908959 1.3136632096 -1.289440808 0.22641964285
## 1371 bad 3.3962277813 -1.4454673610 -0.760429253 -6.67617166794
## 1372 good 1.0348964987 1.6338283533 0.881397711 -2.13156582870
## 1373 bad 3.3962277813 -1.4454673610 -0.760429253 -6.67617166794
## 1374 bad -0.2716957971 -2.7584739457 -0.157535452 0.49066211759
## 1375 bad -1.3947087885 -0.7655760137 -1.712247468 -3.12864488322
## 1376 bad -0.5943041973 -0.9047694820 0.042514273 0.10450583866
## 1377 bad -0.9009568072 0.3938055659 -1.774230707 -0.14371936935
## 1378 good -2.5502177648 -0.1396140191 1.974709385 -0.23587748709
## 1379 good -0.8246809389 0.5559197730 -0.681016753 0.14527225811
## 1380 good -1.3961813008 0.4183199377 -0.308696611 -0.03741882322
## 1381 good -1.3961813008 0.4183199377 -0.308696611 -0.03741882322
## 1382 bad -2.0732483693 0.2649213014 -2.026358000 -0.03043916402
## 1383 bad -0.6590855259 -1.6739708687 0.307382989 0.42361215837
## 1384 bad -0.6590855259 -1.6739708687 0.307382989 0.42361215837
## 1385 bad -0.8648612664 -1.5548294359 -0.755202613 -0.39590202123
## 1386 bad -0.3945438230 -2.6394138499 -0.474698077 0.95540920312
## 1387 bad -1.0243362070 0.2738144716 -1.474427053 -0.58333043672
## 1388 bad -1.0243362070 0.2738144716 -1.474427053 -0.58333043672
## 1389 bad -0.1337890197 -0.6272898749 -0.268404445 0.66610319234
## 1390 bad -0.9036883689 -2.3360884800 1.035862862 0.06195834104
## 1391 good -3.2493077164 1.2826117836 1.375575551 -0.09271197384
## 1392 bad -0.7894492038 0.8078570235 -0.661301590 -0.03737311439
## 1393 bad -1.4837301863 1.0973887075 -1.406805783 -0.18070813957
## 1394 bad -0.5610009514 -0.3034944970 -0.036882824 0.46563346751
## 1395 bad -1.0270427428 -1.9779575858 -0.103838661 0.78989107789
## 1396 good -0.3630314407 0.6338176233 -2.092447872 -0.51286153860
## 1397 bad -0.3347959415 0.6300244705 -2.039273230 -0.46384325000
## 1398 bad -0.6916598187 -1.1990803841 0.180190371 0.16642395930
## 1399 good -1.5399441741 0.1478932169 -0.644242404 -0.23517686539
## 1400 good -1.8005059097 0.6997346946 -0.408156430 -0.40967196528
## 1401 bad -0.6474162718 -2.8443704880 0.758616938 0.37804686973
## 1402 bad -0.6474162718 -2.8443704880 0.758616938 0.37804686973
## 1403 good -0.1264011949 2.1303694557 1.261047506 -0.03363859979
## 1404 good 0.6557880731 1.7758575073 0.430454556 -1.52776194890
## 1405 good 0.1707566861 0.1886856345 -0.120896596 0.41634063364
## 1406 good 0.0559714824 1.2235559275 1.731903108 -0.57605138678
## 1407 good 1.4233000025 1.0426183191 1.012624906 0.31533742544
## 1408 good -2.1582453922 -1.1205546892 1.925562681 -1.09165483515
## 1409 good 0.2375777310 0.5703790549 3.377454474 -0.41157416522
## 1410 good -2.1582453922 -1.1205546892 1.925562681 -1.09165483515
## 1411 good -3.4547585137 1.1054345886 -1.153149546 -0.13110364765
## 1412 good -0.9528199722 1.1327578201 0.239602071 -0.28873943262
## 1413 good 1.4233000025 1.0426183191 1.012624906 0.31533742544
## 1414 bad 1.4580366017 -0.4713086835 0.178340002 -1.03760405590
## 1415 bad 2.5815682244 1.3547459728 -0.727598549 0.16745695476
## 1416 bad -1.8702910556 0.3824315001 -0.970759728 -1.12167370122
## 1417 bad 2.5815682244 1.3547459728 -0.727598549 0.16745695476
## 1418 good -0.4825113340 1.0854767452 1.996181654 -0.00757808274
## 1419 bad -1.0441771765 1.0351366761 -1.477357473 0.04890176427
## 1420 bad -0.9109256818 -2.5424661512 0.808279996 0.57005920800
## 1421 bad -1.0441771765 1.0351366761 -1.477357473 0.04890176427
## 1422 bad -0.5688723260 -0.6761741766 0.136618142 -0.05667688185
## 1423 good -1.4969738227 -0.9525553611 1.716133062 -0.76389653021
## 1424 bad -1.2460980581 -0.0838995212 0.044500287 -0.45782089167
## 1425 good 0.8571556323 1.1682781493 -0.248147670 -0.21157992252
## 1426 good 0.8571556323 1.1682781493 -0.248147670 -0.21157992252
## 1427 good -0.1308745432 0.7552146395 2.167987836 0.58432340359
## 1428 bad -0.3164205011 1.5038834185 0.198951101 0.28509028391
## 1429 bad -1.3217307733 -0.6677960211 0.254383605 -0.01611395027
## 1430 bad 0.4685956599 -0.1777717021 3.199145478 -0.13228731104
## 1431 bad -0.8152133251 0.3175194965 0.559567932 0.05272447217
## 1432 good -0.2449836197 -0.1541055529 0.225703061 0.36170104085
## 1433 good -2.3231884793 2.1288181987 0.573597571 -0.77628320343
## 1434 good -1.2948001488 0.8312133293 0.172366866 -0.67404757947
## 1435 good 4.6892061608 -6.8077363899 0.970894380 2.21847685837
## 1436 good 4.6892061608 -6.8077363899 0.970894380 2.21847685837
## 1437 bad 2.3434472548 -1.9444477029 -0.313871232 -0.95566473854
## 1438 bad -1.8120340184 -0.5547497094 -0.392515624 0.84688057143
## 1439 bad -2.0224343646 1.1830042465 -1.483454017 0.40502686596
## 1440 good -1.4877941273 -1.5232056067 0.999764694 -0.15679864361
## 1441 good -0.3571418001 1.3467742162 1.174313068 -0.18730006205
## 1442 good -0.0599608098 -2.4777923634 -0.601020455 0.79314394054
## 1443 bad -1.2369280491 -0.3642189640 -0.722674897 -0.71120477379
## 1444 bad -2.1534481272 1.6885989125 0.267931018 0.27935119782
## 1445 good -1.4913359468 -1.5111261376 1.014518301 -0.15297690500
## 1446 good -0.0635026292 -2.4657128943 -0.586266848 0.79696567915
## 1447 bad -1.2369280491 -0.3642189640 -0.722674897 -0.71120477379
## 1448 bad -1.3910351408 -0.8308966593 -0.633339175 -0.74152276886
## 1449 bad -1.3227198611 -1.8019445994 0.498676941 -0.57802713731
## 1450 good -0.3734247018 1.3293611021 1.204488099 -0.20545372765
## 1451 good -0.3571418001 1.3467742162 1.174313068 -0.18730006205
## 1452 good 0.8102833202 1.3958112381 0.493802827 -0.43950567979
## 1453 good -2.1085423088 -0.5314974509 1.868573698 -0.51240683680
## 1454 bad -0.3251825938 -1.5653173314 0.254398253 0.17657273679
## 1455 good 2.7308938355 1.3805485691 -0.505967483 0.90552839900
## 1456 good -2.9693557806 0.6577629450 -1.097968795 -0.36156719340
## 1457 good -2.5892628216 -1.3381271043 1.798787690 0.19714608754
## 1458 bad -0.3251825938 -1.5653173314 0.254398253 0.17657273679
## 1459 bad 0.0098636151 1.6421843514 0.243786908 0.30886373751
## 1460 good -0.1555385574 2.6203699871 1.563745943 -0.18841994388
## 1461 good -1.1960924670 -1.2081989187 1.123364155 0.19942668968
## 1462 bad -2.5810432838 0.3318883554 -1.681184061 -0.20198505004
## 1463 good -1.7556814658 0.1213814351 -0.578159795 -0.12621822091
## 1464 good -1.8467769811 0.1297684495 -0.372611307 0.09051792819
## 1465 bad -1.5574628011 -1.2358038222 0.443666696 -0.29720461764
## 1466 bad -1.5574628011 -1.2358038222 0.443666696 -0.29720461764
## 1467 good -0.1368112793 -0.8819105160 0.664196058 0.24894742822
## 1468 bad -2.5671862185 -0.6170156920 -1.160668456 0.04013778798
## 1469 good -0.1368112793 -0.8819105160 0.664196058 0.24894742822
## 1470 bad -1.6037199010 -1.3330660436 -1.673249211 0.15107054056
## 1471 bad 0.5797231217 0.5731632411 -1.959538625 0.20464080820
## 1472 bad -2.5811277420 1.4035306270 0.345940550 0.56426099633
## 1473 good 0.6147285146 0.5049920382 1.777519176 -0.00078659886
## 1474 bad -2.4556203915 0.5103885109 0.324527413 -0.23534174703
## 1475 bad 4.5594461590 -5.6771169476 0.839077117 1.83524791039
## 1476 good -3.0360079427 1.0069082423 3.365315636 -0.73453149383
## 1477 bad 4.5594461590 -5.6771169476 0.839077117 1.83524791039
## 1478 good -3.0395497622 1.0189877114 3.380069243 -0.73070975523
## 1479 bad -1.1545766694 -0.4615676818 -2.197382075 0.97032104984
## 1480 bad 1.3144229861 1.0761152852 0.422172273 0.15837366466
## 1481 bad -2.9732528888 0.8642507488 -0.884846985 -0.81065680365
## 1482 bad 1.3144229861 1.0761152852 0.422172273 0.15837366466
## 1483 bad 0.2738955204 1.2206516426 -0.974023916 -0.33173514633
## 1484 bad -0.1859145445 2.0987485004 0.287472538 0.48527189315
## 1485 bad -2.4740905573 0.8014184107 -1.518462274 -0.25273422309
## 1486 bad -1.2880899422 0.3162957159 -1.253320139 -0.04957974180
## 1487 bad -1.4277231867 0.6046158025 -0.752004790 -0.15684784078
## 1488 bad -2.2994366213 0.8500228019 -0.283836873 -0.90588494327
## 1489 bad -3.4877456561 1.6590556433 0.034631571 -0.07539180329
## 1490 good -2.0016517136 1.5074702608 -0.163133563 -0.50612430096
## 1491 good -0.9671066548 2.8408949399 1.904473641 0.73401402052
## 1492 bad -3.4877456561 1.6590556433 0.034631571 -0.07539180329
## 1493 bad -3.2643434005 1.7103950961 0.071656928 0.05500916248
## 1494 bad -0.1850186303 -2.2653950152 0.846204901 -0.04526838082
## 1495 good -1.5611512894 0.8397174348 0.515323126 -0.63243828575
## 1496 good -1.2422290457 0.7024797767 0.455756617 -0.96062562715
## 1497 bad -0.1850186303 -2.2653950152 0.846204901 -0.04526838082
## 1498 good -2.3204431296 1.0951880753 -0.559712602 0.02684213900
## 1499 good -2.5959923564 0.4940424189 -1.468016497 -0.12569655629
## 1500 good -2.3204431296 1.0951880753 -0.559712602 0.02684213900
## 1501 bad -1.4976477121 0.5150558057 -1.698868730 -0.30066977816
## 1502 bad -0.0470169618 -1.6998405707 -0.819041211 0.64430164450
## 1503 bad -0.8003947022 -0.5646533603 -0.482651799 0.18971249650
## 1504 good -1.0670181044 1.5360032105 0.408304398 -0.27274674177
## 1505 good 0.2064206766 1.9405632338 0.355260362 0.07229798397
## 1506 bad -2.2967191374 0.5034306312 -1.585236897 -0.58290807202
## 1507 good -2.2910625930 0.7717695094 -1.152547150 -0.44747363917
## 1508 good 0.2064206766 1.9405632338 0.355260362 0.07229798397
## 1509 good 0.8244068759 0.9207069525 0.923360374 0.09349582178
## 1510 bad -0.0051054204 2.3080968063 1.267693186 -0.05285550771
## 1511 good -0.9553237634 -0.4698478339 0.916997066 -0.13218601079
## 1512 bad -1.4713075764 -0.4518279652 -0.934617188 -0.24672668750
## 1513 good -2.4097972658 0.2813754038 -1.052503892 -0.28007809214
## 1514 good -1.9227261606 0.0160894230 0.729095839 -0.50755037876
## 1515 good -0.9023094109 -1.8368306408 -1.076206501 0.09633934070
## 1516 good -0.9040803207 -1.8307909062 -1.068829698 0.09825021001
## 1517 bad -0.4806482284 -0.1096437981 1.782723820 -1.02694971668
## 1518 good -1.5549756753 -0.4836808583 0.976979269 -0.81362600032
## 1519 bad 0.2330035473 1.1090945086 -0.217543814 -0.47369150034
## 1520 bad -1.5594226597 -0.2551013094 -1.006359388 -0.43236407052
## 1521 good -1.5549756753 -0.4836808583 0.976979269 -0.81362600032
## 1522 bad -1.1810615183 0.3717557351 -1.206861146 0.16318331201
## 1523 bad -0.4806482284 -0.1096437981 1.782723820 -1.02694971668
## 1524 bad -1.0217676905 -0.6387393066 1.030855850 -0.04483681730
## 1525 good -1.1581174192 -0.1522917808 0.895816438 -0.81504550648
## 1526 bad -1.2733634284 -0.0232026466 -0.141776298 -0.19535541287
## 1527 good -1.1064426087 -0.1240041209 -0.118102508 -0.19088775567
## 1528 good -1.3206323043 0.7499760888 0.008863330 -0.37526488999
## 1529 good 0.8517232192 -0.0839109003 0.992576834 -0.07624924419
## 1530 good -1.7641091772 -1.7079499009 0.552771686 0.13906732285
## 1531 good -1.7790964684 1.4630178963 0.479699266 -0.27720691492
## 1532 bad -2.1578830176 -0.1542485536 -0.673795152 -0.37621681136
## 1533 good -0.7055733266 0.2772028936 -0.282678560 -0.17579631401
## 1534 bad -0.0460816996 -1.6440383263 0.658128305 0.19313569136
## 1535 good -2.0419684423 1.0767779189 0.669375867 -0.01290598831
## 1536 good -1.1695315847 -0.0560102960 -0.544961263 -0.10860848762
## 1537 good -2.3071765187 -0.3811115597 0.564476807 -0.57365705850
## 1538 good -2.5027326470 0.0067722527 0.153980356 -0.38920877587
## 1539 bad -2.4502594340 0.7004748077 0.414012510 -0.19439485668
## 1540 bad -0.3554878757 -0.9498452614 1.307020655 -0.27051011565
## 1541 good -1.8471046339 0.3560750874 0.379639049 0.54176819654
## 1542 good -0.2133702694 0.5587022334 1.409004057 0.12663574933
## 1543 good -2.2802935375 -0.7533691079 -0.216621943 -0.08240180148
## 1544 good 1.3232082622 1.0058691582 -0.446324357 1.03144571191
## 1545 good 0.7447041003 1.6357973064 0.953979830 -0.13993580375
## 1546 good -1.1571560058 0.3739221984 -0.125338430 -0.18195351607
## 1547 bad -1.5614873752 0.1374009610 -0.463193165 -0.19536595759
## 1548 bad -3.1885468407 1.4984504289 0.652255594 0.24361531790
## 1549 bad 2.2298161376 0.2600997205 0.086076615 0.58692455589
## 1550 good -0.5004563897 1.4234126852 1.255669667 -0.32123299365
## 1551 bad -1.4890130632 -0.6785337045 -1.560825015 0.00241598343
## 1552 bad -1.4745282920 -0.7320307839 -1.481590143 0.01057999744
## 1553 good -2.0926431024 -1.3409197909 0.853883063 -0.26023162619
## 1554 bad -1.3631894565 -0.7894070876 -1.557362418 -0.17313296202
## 1555 good -2.5542057660 -0.0056678512 -0.902541088 0.03875139526
## 1556 good -1.5402196223 0.6967265816 0.045902670 -0.36226661577
## 1557 bad -2.4275701206 -0.2214161690 -1.688313635 -0.07730646436
## 1558 good -2.5542057660 -0.0056678512 -0.902541088 0.03875139526
## 1559 bad 1.0292631251 -5.6068195495 1.844775333 -0.60082435162
## 1560 bad -0.3150794941 -2.3814772725 0.863797119 0.34802342863
## 1561 bad -0.3150794941 -2.3814772725 0.863797119 0.34802342863
## 1562 bad -0.3150794941 -2.3814772725 0.863797119 0.34802342863
## 1563 bad -1.3306207562 0.3678444641 -1.062474812 -0.07550788800
## 1564 bad -1.3306207562 0.3678444641 -1.062474812 -0.07550788800
## 1565 bad -1.3306207562 0.3678444641 -1.062474812 -0.07550788800
## 1566 good -1.8974422210 -0.2948280491 0.669052718 -0.77647806091
## 1567 good 0.2464746288 0.9444442788 2.666801505 0.17983586926
## 1568 bad -1.3306207562 0.3678444641 -1.062474812 -0.07550788800
## 1569 bad -1.0739319797 -0.8026188483 -0.437392377 -0.27274838949
## 1570 good -2.4019061267 0.9801341868 0.787333165 0.09111462862
## 1571 good 0.2004534301 1.1880823298 2.532017177 -3.14449418183
## 1572 good -1.7110684769 1.0754423685 0.796794189 0.30173324397
## 1573 bad -0.7865023364 -2.4023622878 0.515118895 0.46253595865
## 1574 good -2.3578747948 0.7657890702 1.371882295 -0.20382053013
## 1575 good 1.3793294014 -2.7475411092 2.708838175 3.80887843777
## 1576 good -0.5248894680 1.6063232880 0.767666690 0.23327565381
## 1577 good 0.9133955317 1.3784017662 1.332889639 -0.40303221259
## 1578 good -1.9040854922 0.1044412212 0.205234058 0.71752166779
## 1579 good -1.5199255901 0.7192436455 -0.197821709 -1.03955409599
## 1580 bad -2.7479381549 0.4293182079 0.914052454 -0.03978364640
## 1581 good -0.7819785653 1.8140914552 1.073577640 0.34019195257
## 1582 bad -2.7479381549 0.4293182079 0.914052454 -0.03978364640
## 1583 bad -3.2071294065 0.8892051944 0.292123484 0.42720843172
## 1584 bad -0.8296831154 -1.6877582571 1.346795201 -0.08236375594
## 1585 good -0.2079615517 0.9698122031 2.186187399 -0.19726320969
## 1586 good -0.0399990188 0.6553822038 2.042178291 -0.30565977267
## 1587 good -0.0714585911 -0.0145811063 2.773191284 -0.26404600999
## 1588 good -2.5227525478 0.4140461589 0.317927670 -0.45889444107
## 1589 good -0.9721684607 -0.7977905011 2.848003891 -0.14827314277
## 1590 bad -0.2367277732 -3.2310056803 -0.112595495 1.67338241667
## 1591 good -1.7259228705 0.5287742419 1.648525655 -1.06685101625
## 1592 good -3.4853588000 0.5797807980 0.176133382 -0.60625729738
## 1593 good -1.4556735759 -0.3116480975 1.123887808 -0.49172292540
## 1594 good -1.2352310829 -0.9250562087 -0.009306277 -0.82173750284
## 1595 bad -2.1498275430 -0.8140313395 0.616869627 -0.40755913580
## 1596 good -2.2138035086 -0.8928220455 1.806836694 -0.41387351155
## 1597 good -1.4556735759 -0.3116480975 1.123887808 -0.49172292540
## 1598 bad -2.2698078356 -0.9794846868 0.627768172 -0.63956998507
## 1599 good -0.4268412180 0.5365223591 1.628445751 0.39159343916
## PC5 PC6 PC7 PC8 PC9
## 1 0.0669935224 0.9136348704 0.1609928261 -0.28217000106 0.0050964416
## 2 -0.0183858093 -0.9294231538 1.0095127630 0.76234847222 -0.5205438218
## 3 -0.0435173927 -0.4013475722 0.5393847364 0.59775905227 -0.0868297617
## 4 -1.4986802172 0.1309762484 -0.3441828194 -0.45523257867 0.0915479380
## 5 0.0669935224 0.9136348704 0.1609928261 -0.28217000106 0.0050964416
## 6 -0.1100415444 0.9933156118 0.1096249482 -0.31393599480 -0.0343367725
## 7 -1.1330279031 -0.1749456402 -0.2609303432 0.24007419473 -0.0272946674
## 8 -0.7801905006 -0.2859675867 -0.1314080335 0.11929993235 -0.6144533503
## 9 -0.2266557216 0.5124735597 -0.2495319898 0.43885122138 -0.3988740491
## 10 1.7179633422 0.4763482607 -0.2302975166 0.83852617137 1.2736043546
## 11 -0.9320478027 -0.2025355470 -0.7160007726 0.51987186420 0.1902283034
## 12 1.7179633422 0.4763482607 -0.2302975166 0.83852617137 1.2736043546
## 13 -0.3430536582 0.2950619453 -0.6246158758 -0.30841489320 0.3445767022
## 14 0.7261869420 2.1989902806 2.0131326263 2.06982921902 0.4943668514
## 15 -0.1993024802 -0.4024399403 0.5196568861 0.07760989398 -0.8783552909
## 16 -0.1028980214 -0.2381892414 0.6718617328 0.22045820032 -0.7022863170
## 17 -1.2901100690 0.4181238813 -0.0899748607 -0.79445926944 0.2313549398
## 18 1.1297066055 -0.7777633105 -0.6911672555 0.01562143859 -0.3951800058
## 19 0.8988497518 0.4782637317 -1.2129477788 0.63449602095 0.1157604877
## 20 0.2596692654 -0.7562733175 -1.9293907268 -0.27733437474 -0.2354699527
## 21 -1.5624172316 0.9905076526 -1.1296424612 -1.02361101034 -0.3252315137
## 22 -0.3387474977 1.5309025464 -0.4136891929 -0.89034698714 0.3139183080
## 23 -0.5429995518 0.5873450351 -0.0428989909 1.25009264492 -0.0279746552
## 24 -0.9303275820 -0.3368931813 -0.6019113663 0.86425856230 0.2721515995
## 25 -0.2336205748 1.0728422215 -0.7813198438 -0.01364988878 -0.4671969963
## 26 -0.9908216479 0.6524004082 -1.2968697506 0.44341632572 -0.1432720680
## 27 -0.5966649746 0.4702720007 -1.0195882830 0.51704723720 0.0204130781
## 28 -0.5429995518 0.5873450351 -0.0428989909 1.25009264492 -0.0279746552
## 29 -0.0705952129 0.6000298313 0.0481523997 -0.14506116545 -0.1682786060
## 30 0.0613867659 0.2469258490 -0.0204644331 0.36007543369 -0.3652916989
## 31 -0.2795798503 -0.5436280865 -0.1383670913 0.23076627378 0.4847768706
## 32 0.5698943351 -0.0314400213 0.0737464786 -0.41960291047 -0.7160569121
## 33 -0.8416209810 -0.7688550420 0.4980901807 0.94039076542 0.9317315103
## 34 3.6874514768 0.6845659515 -1.5953934920 0.87256060614 -0.7876094114
## 35 -0.8963308050 0.7615045227 -1.9724047621 0.12659170074 0.4543846931
## 36 2.0305476400 0.5882268137 -0.7663372463 0.73482155382 -0.1843298387
## 37 0.7790861560 0.3043374653 0.0367927658 -0.20640168952 0.1500749117
## 38 -0.6362980105 0.7114394171 -0.3502178567 0.78083651761 -0.1715635810
## 39 0.7946503742 -1.6945776941 -0.1414330871 -0.95645728209 0.4180257476
## 40 1.7610831167 0.6457733831 -0.4244505009 1.04386400104 1.2761353208
## 41 1.7610831167 0.6457733831 -0.4244505009 1.04386400104 1.2761353208
## 42 -0.3912454745 -0.1844476623 -0.3885732829 -0.13256739325 -0.0931503836
## 43 1.8428924372 -1.1123866979 -1.7060586295 -0.58902943130 -0.7896580254
## 44 0.8449025161 1.1805112666 1.8447393176 1.50831440039 0.2389138710
## 45 -0.0940272486 0.7123152074 -0.1913386509 0.23664824741 0.0696675229
## 46 1.2866801381 0.5910644470 -0.0296121438 -1.53801944745 1.6137555308
## 47 -0.4993675107 -0.9652550439 0.9511105807 -0.17960794145 1.4553010557
## 48 -1.1152696128 0.2995516297 -1.3309843835 -0.52115060123 0.2209052982
## 49 -0.8161247216 0.8158026330 -1.3177518958 0.53538263888 0.1886237432
## 50 -1.7080257597 0.5302088851 -1.5138857125 0.36124306941 1.5454917571
## 51 -0.5678609676 -0.0026347123 0.4220251976 1.02848862356 0.2479483839
## 52 -0.1140969907 0.7985892013 -0.6842703707 0.79927762585 -0.1732866979
## 53 -0.1708214169 0.8239138938 -0.7248035424 0.90410833846 -0.0876420664
## 54 -1.3593925445 -0.1612593047 -0.6814840927 0.14041707634 0.3756716120
## 55 -0.5967771193 -0.8389693675 -0.3755714706 0.53487713848 -0.9283941638
## 56 0.3824608565 0.2445387242 -0.3821954761 0.25201988919 -0.8877579874
## 57 -0.2765607270 -0.3782056504 -0.7209326097 0.85006490878 0.1207951250
## 58 0.5666357332 0.1556766862 0.1822517832 0.51116855269 -1.1272912266
## 59 -0.0854785659 0.5211241455 0.0809992422 -0.37153525324 0.1325970136
## 60 -0.5348289818 0.7493670988 -1.2401127057 -0.04652142596 0.6413204327
## 61 -0.6219590659 1.3372786029 -0.4569174923 -0.72650152081 0.2017230630
## 62 -1.0549251981 -0.6662783583 0.1883036926 -0.13657491110 1.4297119061
## 63 -0.3761920451 0.6573768206 -0.4212779304 0.10112198706 0.0002925230
## 64 -0.1367326438 -0.0451313160 0.1985016921 0.13096202427 0.2710810930
## 65 1.7949667354 -0.7858223236 -0.7693272156 0.14745694665 -0.0863497634
## 66 1.7949667354 -0.7858223236 -0.7693272156 0.14745694665 -0.0863497634
## 67 -0.5632305732 0.7214389465 -0.3326083378 -0.07391349083 0.0776176914
## 68 0.3387903466 0.0417455576 0.2256504956 -0.07927287450 0.1007701248
## 69 -0.8094449055 0.4866439416 0.2516539172 -0.47905893403 0.0190912206
## 70 0.6203325432 0.5177744431 1.4046345265 1.08097109383 -0.1149557282
## 71 -0.4848726731 0.1133224356 -0.1583328266 0.24632639556 -0.4959177303
## 72 -0.9330671882 -0.6385645667 -0.2408080491 -0.17890300549 0.9025302412
## 73 -1.0577748940 -0.6930287976 -0.1082743592 -0.15221065942 0.8013864587
## 74 -0.4685599730 0.0123109473 -0.1431788882 -0.15263482151 0.3195207538
## 75 -0.7891097954 1.0056925038 0.0267602228 -0.41544858636 0.1617247660
## 76 0.2888728295 1.2324392194 -0.2842322165 -1.77317881354 1.1363049655
## 77 0.2888728295 1.2324392194 -0.2842322165 -1.77317881354 1.1363049655
## 78 0.9189090648 -0.1031519365 0.2588078202 -0.59730804752 -0.2151705774
## 79 -0.3114936542 -0.6424170233 0.0548448799 -0.01799603278 0.9394296310
## 80 -1.1411520013 0.6727273591 1.7563877818 1.53956713806 0.6228160277
## 81 -0.5893818080 0.9255103210 -1.2186619168 0.35552009889 0.4326697624
## 82 1.4173189258 -1.1188419481 -1.9768391041 -1.67648749615 -0.0160856126
## 83 -1.0421394361 0.4401410584 -0.7290279022 -0.60260523016 0.7114062008
## 84 1.5078743496 -1.3319200410 -1.1295986086 -0.66866130786 -0.4329846938
## 85 -0.5814523519 1.2305404302 -0.6074682109 -0.21602937583 0.8458418179
## 86 -0.1885629251 0.3486602282 -0.2989467836 -0.04276367837 -0.2375442714
## 87 -0.0168453776 1.4666791644 3.4223902577 3.92494428944 1.3144762657
## 88 -0.4399098054 0.8436314736 -0.3213360737 0.14377090849 0.3332529311
## 89 -0.9617258527 1.0035849703 1.3239163085 1.00105231577 0.4196014939
## 90 -0.1980677618 1.0999128084 -0.4496160239 -0.20505702658 0.1225968990
## 91 -2.0230741737 -0.5279197661 0.0138727501 -0.19517190094 0.0472328535
## 92 -0.0168453776 1.4666791644 3.4223902577 3.92494428944 1.3144762657
## 93 0.0746635382 1.5721263197 3.4251193514 4.03139300506 1.3541280916
## 94 -0.4399098054 0.8436314736 -0.3213360737 0.14377090849 0.3332529311
## 95 -0.3252924307 -0.2199558423 1.2418549641 -1.45178423323 -0.0084352332
## 96 0.9684618324 0.1508794197 0.3803743492 -1.42604544453 1.8489209657
## 97 1.2301229947 -0.3021484146 0.0402043343 -0.04741183584 -0.0538956606
## 98 -0.5580175328 0.4166833889 -0.8022793710 0.90968058683 0.4437501393
## 99 0.6967905571 -0.2974689668 0.4864480799 0.19567614668 0.0342580438
## 100 -0.7314036129 0.5060380427 -0.3962196721 0.28452261035 -0.1422978897
## 101 -0.0489460385 0.1804226124 0.1585090838 -0.50148786267 0.5714185204
## 102 -0.3405735462 -0.1146269549 -0.4736342241 0.00687031714 0.1406242729
## 103 -0.7314036129 0.5060380427 -0.3962196721 0.28452261035 -0.1422978897
## 104 -0.9060812920 -0.0756659497 -0.4682944776 0.17813187222 0.5244541292
## 105 -0.6184534584 0.1723856215 -1.1140711539 0.29932591040 0.5556218668
## 106 -0.9060812920 -0.0756659497 -0.4682944776 0.17813187222 0.5244541292
## 107 1.2702113291 -1.1802131044 -2.0156203078 -1.33354398662 0.1639116862
## 108 -0.2473903241 1.0397394174 0.1068414792 -0.09964071538 1.0033501267
## 109 -0.4737821372 1.1973988517 -0.4867810951 -0.28033501489 0.9990281644
## 110 -1.3806277341 -1.1422377387 0.7018373438 -0.65383374442 1.1921006062
## 111 -0.3334410710 0.3399727787 0.3985793735 1.26302069217 0.1307283912
## 112 -0.8834191697 -0.7802448943 0.3627254035 1.14836602138 0.9682208029
## 113 -0.9715536058 -0.7527428438 0.3318061764 1.07320432892 0.8427799001
## 114 -0.6903488084 1.1307211836 -0.2118292054 -0.64640799862 -0.3693260825
## 115 -0.3334410710 0.3399727787 0.3985793735 1.26302069217 0.1307283912
## 116 -0.8853051360 -0.7912436845 -0.2258688204 0.66181604875 0.0411146965
## 117 -0.2345389909 0.6004934750 0.0196133010 -0.42600064313 0.3090140031
## 118 -0.3634633560 0.3721520888 -0.6237958221 0.03453822528 0.0508149057
## 119 -0.1142226817 -0.9784705093 0.0430561575 0.33088589210 -0.4178540980
## 120 -0.9767507697 -0.3599486519 -0.1716394667 0.09954986744 0.3080875392
## 121 -0.0450792698 -1.7282780255 0.3036394955 -0.30379793715 1.0767300242
## 122 -0.1142226817 -0.9784705093 0.0430561575 0.33088589210 -0.4178540980
## 123 0.4985507479 0.9445248738 -0.2910913665 0.00970003561 0.0891188932
## 124 0.2901539949 0.4258207463 0.0577911982 -0.03607686831 -0.1554725412
## 125 -1.4152845923 -0.0003237576 -0.4896283688 -0.36288248149 0.6114531101
## 126 -0.7441529328 -0.4177681586 0.3763806345 0.20653468799 -0.6207430843
## 127 1.3098927980 -1.4145331376 2.2030632279 -0.93380040044 0.4094750697
## 128 1.3758009406 -1.4073310935 2.1296921704 -0.96838126386 0.4251414653
## 129 0.5427130063 1.0132220179 0.9632775273 0.70434057977 0.3656635080
## 130 -0.5367361107 0.9973823977 -1.3779451915 0.43969337238 0.1875776334
## 131 -1.1861453157 -0.9842423959 0.3875874549 -0.73030050545 1.3652429017
## 132 0.5611409418 -0.1628570514 0.4768459977 -0.47211847177 1.1774380009
## 133 0.5611409418 -0.1628570514 0.4768459977 -0.47211847177 1.1774380009
## 134 -0.7197803959 0.5732233445 -0.4259970349 0.47430413965 -0.5970740640
## 135 0.1351822745 -1.5201218356 1.0396889969 0.55255996313 -0.2548976953
## 136 -0.4077326974 -0.3260477496 1.0431467712 1.05768682442 0.0757092270
## 137 -0.3484237697 -0.0991478874 0.7131055700 0.82743048982 0.3393981492
## 138 -0.4677073511 1.3948456514 -0.8394597929 -0.48072035477 0.5995615699
## 139 -1.0299431159 -0.2032735209 -0.3084730414 0.13050371731 1.0653209125
## 140 -1.1499180063 -0.2433600930 -0.2586505572 0.10818140922 0.9874985933
## 141 -0.4077326974 -0.3260477496 1.0431467712 1.05768682442 0.0757092270
## 142 -0.3484237697 -0.0991478874 0.7131055700 0.82743048982 0.3393981492
## 143 0.7028664127 0.1881708618 0.6206576088 -0.28329086908 -0.2275516434
## 144 -0.6910533240 0.7530972038 -1.2284140343 0.95978269163 0.1680121306
## 145 0.7028664127 0.1881708618 0.6206576088 -0.28329086908 -0.2275516434
## 146 -1.6409864070 -1.1537522970 0.1638958142 -0.59203700826 1.1985509148
## 147 -0.5266319463 -0.1539617182 -0.2383027205 -0.30374423467 0.6901705369
## 148 -0.4343188688 -0.9438571501 -1.1729683559 0.19138231820 0.6588886667
## 149 -0.0109770636 0.4818467603 -0.5741791635 0.19468779468 -0.1232109049
## 150 0.4431557260 0.9910612663 -0.6264723553 -1.13533023272 0.6910903042
## 151 -0.3878298319 0.2794441298 -1.4104556757 -0.22299020665 0.4011142550
## 152 3.2156163535 -1.7492210400 -0.8946456061 -0.69288919118 -0.6445722568
## 153 -0.9805417304 -0.6470435889 -0.0375536151 0.04118713266 0.0810512405
## 154 -0.9805417304 -0.6470435889 -0.0375536151 0.04118713266 0.0810512405
## 155 0.9244538391 0.5238075409 -0.4546593261 0.08656890148 1.2018175509
## 156 0.9383385746 0.4976539887 -0.5029217714 0.07588236572 1.2387538707
## 157 0.9244538391 0.5238075409 -0.4546593261 0.08656890148 1.2018175509
## 158 0.9383385746 0.4976539887 -0.5029217714 0.07588236572 1.2387538707
## 159 0.0230260784 0.6480776253 -0.2955416222 -0.07174754517 -0.2869902710
## 160 -0.4874502278 1.0083095416 -0.2344330852 -0.77714030928 0.9608787970
## 161 0.0051654331 -1.2345863308 0.5543129515 0.84181299695 -0.1438804141
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## 997 1.8183579087 -0.0396824852 0.2762023353 -0.71464694181 0.3867473433
## 998 1.8183579087 -0.0396824852 0.2762023353 -0.71464694181 0.3867473433
## 999 -1.1316632761 -1.2067718353 0.2861708045 0.64744148320 0.2461515967
## 1000 0.6001683334 -1.4461126559 0.4717886717 0.14365966143 -0.1021346022
## 1001 0.4953802824 0.4054086358 -0.0570004906 0.04070099827 0.2621817697
## 1002 -1.0659300950 0.6562099984 0.8195482392 -0.08174800353 -0.9309858788
## 1003 -0.1098365006 0.2059319535 0.3430122599 0.56536824055 -0.2787183678
## 1004 0.1966364286 -0.9442583963 -0.6641693678 -0.05977107355 0.2009444953
## 1005 -1.1637663406 -0.2587520569 -0.3860802037 -0.27380393943 -0.7967354022
## 1006 0.1966364286 -0.9442583963 -0.6641693678 -0.05977107355 0.2009444953
## 1007 -0.1098365006 0.2059319535 0.3430122599 0.56536824055 -0.2787183678
## 1008 -0.0926133581 0.1816069172 0.3530005750 0.54179040379 -0.2292799702
## 1009 0.9260160106 -0.7419178813 -0.5178233685 0.11127650977 -0.2838184040
## 1010 -0.0498916848 -0.7159622469 0.2109083751 -0.27741427010 -0.6757420507
## 1011 -0.4580078348 -0.9873057188 -0.5899643967 -0.16009680366 -0.0558969184
## 1012 -0.8087912086 -0.3758721263 -0.9484615212 0.30941744339 -0.5737923039
## 1013 0.0929103347 -1.7203306970 0.5664080753 0.04842242507 0.0531072214
## 1014 -0.2664435250 -0.0798095235 0.3460396032 -0.12084622364 -0.8969004330
## 1015 0.2756598905 -1.3118358656 -0.4310772286 0.37219102458 -0.2755804925
## 1016 -0.3447179304 -0.6380008060 0.6873485923 0.73251749123 -0.4137203790
## 1017 0.2696288040 -0.5934800005 0.4682272345 0.04361857506 -0.0369385472
## 1018 -3.0479639624 -3.2151238252 -0.6591807574 1.00963586759 -0.5581743964
## 1019 -3.0479639624 -3.2151238252 -0.6591807574 1.00963586759 -0.5581743964
## 1020 0.1582518624 0.9235932565 -0.3163976234 -0.43184079093 0.0576194660
## 1021 0.2021591155 -1.2427858813 -0.5472528677 -1.19273358561 0.1016080827
## 1022 0.2021591155 -1.2427858813 -0.5472528677 -1.19273358561 0.1016080827
## 1023 0.0278567542 0.1535677587 -0.5324982212 0.50050587523 -0.1018559982
## 1024 0.3493272143 -0.8593393251 -0.8755352496 -0.34451699555 0.1096956302
## 1025 -0.1366302085 -0.2933811248 -0.1342327393 0.31448270876 -0.6938604864
## 1026 0.6889995093 -0.4096070112 0.9689929420 -0.04126615601 -0.5088105215
## 1027 -0.1073856086 -0.5595887193 -0.3645696962 -0.52603529316 -0.0037023835
## 1028 0.2496848162 -0.9748871334 0.7129239480 -0.35252375541 -0.5682194095
## 1029 -1.0068543127 0.4042360305 -0.7862637599 -0.37499110962 -1.1945804883
## 1030 -0.1366302085 -0.2933811248 -0.1342327393 0.31448270876 -0.6938604864
## 1031 0.5736996935 -0.4845805907 -0.1249062728 -0.00041576323 -0.4591254786
## 1032 0.1360634135 -0.3602026076 -0.2228161211 0.28899967495 -0.5267420430
## 1033 1.4763182938 -0.0168867600 -0.0426906140 0.36777153198 -0.2270875224
## 1034 0.1613623692 -0.2692111266 -0.3001842649 0.46125287398 -0.5183447164
## 1035 -0.4346294451 -0.8591381065 0.2194055495 0.16505564931 -0.1475919114
## 1036 -0.2084758799 -0.1758039501 -0.2893120579 0.38750925161 -0.2035678370
## 1037 -0.2084336309 -1.2111465843 -0.5441639505 0.02428651541 -0.9108076617
## 1038 -0.4828755397 -0.3585625981 0.7268992829 0.03182602431 0.0630999022
## 1039 2.1610644051 -0.8643418295 -0.1188916086 0.65736856808 -0.5866656373
## 1040 0.2552236825 0.3546282052 0.8488947773 0.04133526890 -0.5954278450
## 1041 1.0580124377 0.3945549755 1.3707140309 -0.66941657988 -0.0852010096
## 1042 -0.4683289092 0.5024996454 -0.5487694533 0.24709093056 -0.0923158869
## 1043 0.2552236825 0.3546282052 0.8488947773 0.04133526890 -0.5954278450
## 1044 3.4347392866 0.6199463189 -0.7017053136 1.19907671176 -0.2201648711
## 1045 0.3996630748 -0.6956348325 -0.5743224254 0.55565014113 0.6961821689
## 1046 -0.7935952036 -0.3771163622 0.0090413108 0.17373847718 -1.5460294095
## 1047 -0.3363638142 -0.6217818004 1.1312341623 0.00268305611 -1.3209117444
## 1048 -0.3990756138 0.8768178668 -0.2125705817 -0.60204992811 -0.5162968915
## 1049 0.0974507834 0.2161228565 0.5104403911 0.13477349317 0.2327026866
## 1050 0.0667997320 0.1774320587 0.4826661348 0.06767588095 0.1039125628
## 1051 -0.3990756138 0.8768178668 -0.2125705817 -0.60204992811 -0.5162968915
## 1052 0.9816453711 -0.9795170580 -1.2736797176 -0.58500974480 -0.2751997351
## 1053 0.9950107517 -0.8540648888 0.0590845268 -0.44342880032 -0.4237576147
## 1054 0.5934029250 -0.1793428347 0.1423470966 -0.15190521965 0.1954680890
## 1055 -0.8782039541 -0.5208801709 -0.1432783334 0.77634042977 1.1163627077
## 1056 -0.8782039541 -0.5208801709 -0.1432783334 0.77634042977 1.1163627077
## 1057 1.3203291649 -1.2691701445 -0.5820088041 -0.11411133276 0.3111430449
## 1058 -0.5177670264 -0.0889055453 -1.1733581198 0.63601106512 -0.2086865655
## 1059 -0.1150293738 -0.7456138692 0.9636680447 -0.70613655279 -0.5017385399
## 1060 1.3203291649 -1.2691701445 -0.5820088041 -0.11411133276 0.3111430449
## 1061 -0.9076945445 0.6147287303 -0.3405004283 0.04171627492 -0.0372011035
## 1062 -0.0171038180 -1.0029122621 0.2622833774 -0.05983686995 0.1540851536
## 1063 0.0529030882 -0.2783700425 -0.6002333210 -0.32444160436 0.2317224583
## 1064 -0.1150859035 -0.5702325625 -0.3679846021 -0.45348874395 0.3155925288
## 1065 0.4124898297 -1.4309841785 0.7072703081 0.46449092245 -0.2171544078
## 1066 0.1662613084 -0.2091365161 -0.2179285969 0.43614514629 0.0547116277
## 1067 0.8044829293 -0.5362596370 0.3402674084 0.48705477433 -0.2603412518
## 1068 -0.5570087023 -0.4034113113 0.2685784232 1.14856133557 -0.0299202809
## 1069 -0.5570087023 -0.4034113113 0.2685784232 1.14856133557 -0.0299202809
## 1070 0.1024292834 -0.3927473810 0.3141869741 -0.51795830940 -0.4084894168
## 1071 -0.8668905459 0.0922811427 0.6116752634 0.65842945320 -0.2510874827
## 1072 2.2579143373 -0.6012143918 -0.7509272255 1.17281862383 -0.0944056512
## 1073 -1.2707887914 0.8862147084 -0.7605193140 -0.54247667049 -0.4354684256
## 1074 0.2814286873 -0.3635382949 0.0351504246 -0.38271801531 -0.0053469967
## 1075 2.2579143373 -0.6012143918 -0.7509272255 1.17281862383 -0.0944056512
## 1076 -0.8476337546 1.8084773166 0.4857437369 -0.73574109731 -1.3028682176
## 1077 -0.2942419416 -0.4696839658 -0.1494314909 0.31847147529 0.1974163231
## 1078 2.2348312085 0.0510793434 -1.5165939164 0.09242891355 0.5763136059
## 1079 2.2348312085 0.0510793434 -1.5165939164 0.09242891355 0.5763136059
## 1080 0.1191146246 -3.2190927822 -1.1645337088 1.40922599325 3.1902339442
## 1081 -1.0187876349 -0.9163922279 -0.4027471454 -0.15509922596 0.1094105396
## 1082 0.0447238056 -3.2646732136 -1.1332135726 1.41559150795 3.3882339616
## 1083 -1.1821498145 0.9861868452 -0.6164154511 -0.57804129363 -0.2557392050
## 1084 -0.0339789120 -0.1031094024 0.7550575243 -0.62938157236 -0.4764679615
## 1085 -1.1821498145 0.9861868452 -0.6164154511 -0.57804129363 -0.2557392050
## 1086 -1.8160741366 -0.7079818937 -0.6272869506 0.70505863739 -1.5737226055
## 1087 1.6865810608 0.6165407422 -0.5984630559 0.18340835018 0.2970659101
## 1088 -0.9726308358 0.2155887271 -0.6905243227 0.21956815081 -0.2626749263
## 1089 -1.4272268252 -0.4511165990 0.4857081539 0.10871617338 -0.6758854894
## 1090 -1.4272268252 -0.4511165990 0.4857081539 0.10871617338 -0.6758854894
## 1091 -1.7708944614 -1.7205648790 0.0235728524 -0.02832431749 -1.3244884651
## 1092 -0.0319350548 -0.0631630315 -0.7313366226 -0.43489969186 0.1953146873
## 1093 0.4204225311 -0.2296767608 -0.1275501106 -0.04302337658 -0.2827295714
## 1094 0.3949762853 0.4005745454 0.4846035155 0.04314502528 -0.0003661025
## 1095 2.3240594280 -0.8849154251 -1.0029899019 0.39757630199 -0.2458303039
## 1096 -0.3442542582 -0.4645297586 -0.7773505855 -0.16912337263 0.0589956881
## 1097 2.3240594280 -0.8849154251 -1.0029899019 0.39757630199 -0.2458303039
## 1098 -0.9129408868 -0.3101844296 -0.7863769106 -0.00703468376 0.1788913099
## 1099 0.8552618918 -1.8134264090 -1.2124273935 -0.19287816808 -0.1217169403
## 1100 -0.9129408868 -0.3101844296 -0.7863769106 -0.00703468376 0.1788913099
## 1101 -0.3952824577 -1.1209425854 0.2788568923 0.61922174586 -0.6501652901
## 1102 0.1675632413 -0.7120263294 -0.0012113435 0.00589762564 -0.1495503969
## 1103 -0.1928647610 -0.1593972511 -0.5634680628 -0.13874518200 -0.4274119862
## 1104 0.1675632413 -0.7120263294 -0.0012113435 0.00589762564 -0.1495503969
## 1105 0.1648198672 -1.1257969580 0.2031904826 0.05502260589 -0.2900752888
## 1106 0.3625092151 -0.9524421932 0.4126654442 -0.15864686991 0.6256190767
## 1107 -0.4911003715 -0.9710168584 -0.8970435050 0.07800752666 -0.1592971405
## 1108 -0.0190332788 0.2030196440 0.2928192637 0.39123206023 0.0153587741
## 1109 0.8923588190 0.0483675509 -0.1612325236 0.00912137027 -0.0116270641
## 1110 -0.1915966432 -0.7342968926 0.3462085463 -0.99218622502 -0.6736938781
## 1111 -0.5651996455 0.0936967204 0.0573333368 0.96303265909 -0.5127807325
## 1112 0.8214492146 0.8677313126 -0.3074682646 -1.80666145616 0.4179514268
## 1113 -0.7494845899 -0.0247949979 -0.3711483632 0.49160710890 0.6549156446
## 1114 -1.2367608607 0.4006767216 -1.2271903141 0.67345548118 -0.0777109370
## 1115 1.0164714509 -1.3350961695 -0.5482761481 -0.06283792147 0.7759842066
## 1116 0.7500700185 -0.5493133552 0.2593575792 -0.00789126131 -0.5078215012
## 1117 0.7500700185 -0.5493133552 0.2593575792 -0.00789126131 -0.5078215012
## 1118 0.7500700185 -0.5493133552 0.2593575792 -0.00789126131 -0.5078215012
## 1119 0.1747376731 -1.4852771177 -0.2292981449 0.28383840385 -0.6054548858
## 1120 1.3117186889 -0.9673723450 0.1932229234 0.14706094489 0.0500499394
## 1121 0.6578494053 -1.8974660515 0.4121872272 0.67084659544 0.0203552594
## 1122 0.0872804874 -0.5783140990 0.0963300669 -0.04418358320 -0.8666096676
## 1123 -0.4904540617 -0.8425722655 -0.1371631106 -0.17067137225 -1.1093357600
## 1124 -0.6503316511 -0.6721554778 0.3752476083 0.02168937429 -0.7653768827
## 1125 1.2634334793 0.5104902028 -0.1254203360 -0.49460601059 0.1684709491
## 1126 -1.1607128654 -0.6901438096 -0.6547687860 0.68564104095 -0.4103446232
## 1127 0.2594762878 -1.1161233496 -0.9157100569 0.11033088284 0.2878832489
## 1128 0.7514878669 -0.6773942788 0.5513885093 -0.06877673508 -0.4284199747
## 1129 -0.6962831103 0.1186162073 0.2403403246 -0.22195708136 -0.2265481612
## 1130 -0.1152306633 -0.1138661829 0.2718804862 -0.08445680080 -0.3586809601
## 1131 -0.1342832157 -0.0665123071 0.6408097394 0.99165863249 -0.6500370723
## 1132 -3.0187972283 0.0921807608 -1.4026395972 0.00045564925 -0.8892507842
## 1133 0.5675942237 -0.5016493399 0.7897344774 -0.04732010517 -0.0544697572
## 1134 2.2773946417 0.2478326854 -0.7133914368 0.90503566365 -0.4351628828
## 1135 -0.1285851671 0.2152201838 -0.0621157322 0.26603460545 0.0405325501
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## 1138 0.2776615079 -0.1794496137 0.9068375938 -0.20872226994 0.0217566186
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## 1142 -0.2062080334 0.2796353709 0.3673493026 0.40561206643 -0.0247750742
## 1143 0.1235839488 -0.3110110729 -0.2725705932 1.20149223910 -0.1419150465
## 1144 -0.0540827002 2.5675961257 -0.6000875736 -0.43007304672 -0.0492930515
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## 1147 0.4104094143 0.1087545278 -0.5532906082 0.49574523319 -0.2572559298
## 1148 1.9837871145 -0.9218306653 -0.9501132155 0.29176660405 -0.1061493481
## 1149 0.2036145483 -0.0182671999 -0.3772435365 -0.49346190024 0.2770250224
## 1150 -0.2654853803 -0.7428244527 -0.1655218232 -0.43864819450 -0.0803355541
## 1151 0.9223847545 -0.3389356286 0.0521861588 0.46324350048 0.0758981471
## 1152 0.4346102917 -0.6792139983 0.4995943857 0.04091556562 0.8448459011
## 1153 -0.4625874086 -1.0602370043 -0.6577019216 0.34116028952 0.0171778901
## 1154 -0.3864675031 -0.3833881423 0.1596712780 -0.09513496865 -0.3791387231
## 1155 0.2106609733 0.3750109950 0.6404889180 -1.14775655650 -1.8141436741
## 1156 -0.4625874086 -1.0602370043 -0.6577019216 0.34116028952 0.0171778901
## 1157 -1.2289383169 0.2175884033 0.1644017981 -0.85142024001 -0.7577745511
## 1158 0.3154376101 -0.4168252602 0.9059113465 0.76512219093 1.4467010778
## 1159 0.6470387906 1.5745232583 0.6672219127 0.74294590759 0.4923053981
## 1160 -0.2782864316 -0.4986689570 -0.0770963877 -0.17008159357 -0.0881570627
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## 1165 -0.0778926808 -0.2967002240 0.9431042562 0.03856071995 -0.0315468921
## 1166 0.7254142874 -1.4164488132 -1.7012970487 -0.35091009710 -0.5312868998
## 1167 -0.4286717619 -0.7018283887 0.9772708474 1.80595888801 0.2527709572
## 1168 0.2671480431 0.0728198499 0.6489337138 -0.05346530601 -0.6014947441
## 1169 -0.0068695567 -1.0393963777 -0.9661697764 0.83080700834 0.3790055606
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## 1173 0.4532549062 -0.3312771875 0.6056867689 -0.27598104689 0.2050489350
## 1174 -1.0040753840 1.2509725334 -0.5838509166 -0.71965429229 -0.0446985774
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## 1176 0.3750759781 0.4306028858 0.7995542450 -1.12113291720 -1.6946319483
## 1177 2.4476380810 -0.5845923726 0.1031902397 -0.28206945571 -0.2131444896
## 1178 0.9554042594 -1.1441597048 0.7812336050 0.85346920652 -0.2516193692
## 1179 0.7648775624 -0.0513289624 1.5655118821 -0.07793196561 1.0902830304
## 1180 -0.0327653026 0.3146151900 -0.0168101786 0.59241009849 0.3298308224
## 1181 -0.0327653026 0.3146151900 -0.0168101786 0.59241009849 0.3298308224
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## 1183 -0.3596632521 1.0929272728 1.0294848207 -0.65441167372 -1.2605229520
## 1184 -0.9542516234 -0.0916092811 -0.0240199151 0.73286567790 0.1793532872
## 1185 -0.2733771500 -0.5778901773 0.4738346430 0.28652965760 1.8685478686
## 1186 -0.1764734296 -1.2204406024 -0.9363557209 -0.17709833861 0.5437546891
## 1187 4.2563046852 -0.7830866881 -0.5077627141 0.46379540137 0.0021144807
## 1188 -0.1764734296 -1.2204406024 -0.9363557209 -0.17709833861 0.5437546891
## 1189 -0.2733771500 -0.5778901773 0.4738346430 0.28652965760 1.8685478686
## 1190 -0.1619452553 -1.0955641681 0.6569014382 0.12649714331 -0.0303145399
## 1191 1.5705912674 -0.8410496743 -0.5572051393 -0.40696634844 0.4015497722
## 1192 1.5297266716 -0.8635301650 -0.2780159830 -1.07217339069 -0.0556827588
## 1193 0.6523394125 0.7101013954 -0.1728633757 -0.23697292709 0.6060615160
## 1194 1.5344715936 -0.8577017351 -0.2981313180 -1.06197318261 -0.0445391531
## 1195 -0.6012127866 -0.9156399599 -0.1094779341 0.47957743880 0.2375390371
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## 1197 -1.0916360522 -0.3667718490 -0.0487599098 0.39255867504 0.3498670365
## 1198 -0.9075086504 -0.7584589747 -0.5692044097 0.98300470634 -0.0828875192
## 1199 -1.0975316239 0.0297757733 -0.0852818590 1.30177190837 0.1725594406
## 1200 -1.0916360522 -0.3667718490 -0.0487599098 0.39255867504 0.3498670365
## 1201 -0.9075086504 -0.7584589747 -0.5692044097 0.98300470634 -0.0828875192
## 1202 -0.4594218983 -0.4700136419 -0.9196443336 0.23304911775 0.0616894826
## 1203 0.1149658776 -0.0889267748 0.1075730044 -0.17859240768 0.1790851593
## 1204 0.9569026211 -0.9694276425 -0.0325577530 0.43363666954 -0.9728241876
## 1205 -0.1314579765 0.7516016631 0.0234390611 -0.16871779998 0.0278380700
## 1206 -0.1314579765 0.7516016631 0.0234390611 -0.16871779998 0.0278380700
## 1207 -0.1314579765 0.7516016631 0.0234390611 -0.16871779998 0.0278380700
## 1208 0.1017354582 0.1392535918 1.3270760477 -1.01904261769 0.1502544604
## 1209 -0.1314579765 0.7516016631 0.0234390611 -0.16871779998 0.0278380700
## 1210 0.2723234938 0.7705637609 -0.1695499542 0.11981204008 0.4230368113
## 1211 0.2139382996 -0.2187932992 -0.0790079171 0.70499352433 -0.2480076356
## 1212 -0.9067044695 0.5242367017 -0.8136662409 0.57139085404 -0.2859688038
## 1213 0.2139382996 -0.2187932992 -0.0790079171 0.70499352433 -0.2480076356
## 1214 -0.4567347128 0.0677975422 0.0067661832 0.62118591572 -0.0013348494
## 1215 -0.9993750859 -0.7200246602 -0.9018296060 0.05155520025 -0.3226894978
## 1216 -0.6137790147 -0.5367284139 -0.9132129741 -0.51676230229 -0.5495581828
## 1217 -0.6909649897 0.1627571137 0.0240042013 0.36479095630 1.1392883852
## 1218 -0.9848412834 -0.4759394726 0.7463208367 0.12956615087 -1.0670678681
## 1219 -0.3354467106 0.0432423221 -0.3437569772 0.42705931677 0.0235186121
## 1220 -1.2432622416 -0.0059387964 0.7700006298 0.56653824307 -0.3606294997
## 1221 -0.1030288965 -0.1084707858 0.2469936727 -1.10757400209 -0.2037456476
## 1222 -0.1030288965 -0.1084707858 0.2469936727 -1.10757400209 -0.2037456476
## 1223 -1.3660620598 -0.3866401664 -0.3351647507 -0.27885861106 -0.4193483742
## 1224 0.1321924945 -0.2820255058 0.5703593506 -0.13869993861 -0.0500771515
## 1225 -0.2361028023 0.2365479178 0.8069997593 0.33539437648 -0.3565038894
## 1226 -0.9082985497 -0.0478164075 -0.4411159045 0.13558223418 -0.0325582201
## 1227 -0.4459504602 -1.1393601483 -1.0140159225 1.69552927391 -1.3466493542
## 1228 -0.0444840818 0.3296750043 0.1131705625 0.18241341891 -0.1002485496
## 1229 0.5276692424 -0.0720955898 0.5730029177 -0.11922072362 0.8459606429
## 1230 -0.7016934196 0.9418936292 -0.4456929227 -0.19882322465 0.3586320611
## 1231 0.0376323014 0.4766615762 -0.4702387609 0.33674356482 0.3438826124
## 1232 -0.2200846712 -0.6808208906 1.4461785579 -0.39990911405 -1.3280902726
## 1233 -0.7016934196 0.9418936292 -0.4456929227 -0.19882322465 0.3586320611
## 1234 -1.5037312073 0.3918893798 -1.1613646228 0.16658816814 -0.3307715796
## 1235 0.5641052378 -2.0561184889 0.3881447561 -0.03910285645 -0.4016134767
## 1236 4.8779085627 -0.6981106540 -2.8985712656 2.65148706915 1.9605665237
## 1237 -0.5219486984 0.4295511624 -0.1490169030 1.05370414980 -0.4261961844
## 1238 0.5641052378 -2.0561184889 0.3881447561 -0.03910285645 -0.4016134767
## 1239 0.1828111627 0.0982902108 0.0034548375 -0.11440041945 -0.4917680564
## 1240 1.7572647218 -0.4903269871 -0.0301924431 0.56742548791 -0.2539246127
## 1241 -1.9625902003 -1.3293945163 -0.5797626189 0.36292352240 -1.1945232311
## 1242 -1.1856180795 -0.1109324854 -0.3215219716 -0.02567605041 -0.6000572658
## 1243 -0.4748733613 -1.1428687887 0.1379462956 0.01003952800 -0.0395506215
## 1244 -0.3463638210 0.5624216579 0.0036984190 -0.07036244868 1.0740621880
## 1245 3.6149635021 -0.0107635016 -2.6181683651 1.46067907602 -1.3391162217
## 1246 0.1285202790 0.7303858061 0.1677802561 -0.45775439956 0.0068436475
## 1247 -0.9285980469 -1.5922816149 -0.0520336760 0.84716939097 -0.2845092594
## 1248 0.1285202790 0.7303858061 0.1677802561 -0.45775439956 0.0068436475
## 1249 0.0363764912 0.3661277055 -0.2095972525 -0.12749628026 -0.2191070389
## 1250 0.1096665857 -0.1909178833 0.1795274141 0.12391227429 -0.8849456828
## 1251 0.1096665857 -0.1909178833 0.1795274141 0.12391227429 -0.8849456828
## 1252 -0.7364653679 -0.1266911888 -0.0405064972 0.25821641227 -0.5635428481
## 1253 0.4382037378 0.0501487415 -0.2738929965 -0.17721720032 -0.1695997284
## 1254 -0.1970590485 0.2480353149 -0.1475917768 0.09424395259 -0.2587830601
## 1255 0.2517732024 -0.0462771485 0.9376751570 -0.06399575470 -0.5687913327
## 1256 -0.0776852742 -0.6819551429 -0.3229474943 -0.17871031435 -0.6268937374
## 1257 -2.0182731523 -1.2342146005 -0.6134521275 0.22996033936 -1.5998590689
## 1258 1.4178801550 -0.3656325581 -0.2888841353 0.67780446338 0.8453631400
## 1259 1.0254942572 -0.4614509934 -0.0926596865 -0.13187424444 -0.4401696205
## 1260 1.0254942572 -0.4614509934 -0.0926596865 -0.13187424444 -0.4401696205
## 1261 0.8977173724 -2.0441880618 -1.2474801077 -1.04570087213 -0.0164081496
## 1262 1.0071783396 -0.8305035078 1.0864977411 -0.71165978266 -0.1455356026
## 1263 -1.2056197241 -0.3627609451 -0.0543944092 -0.15548129613 -0.9271373538
## 1264 0.0642108882 -0.1417739810 0.1704471051 -0.23032264132 -0.5178182269
## 1265 -0.4238972005 -0.0544540377 0.3866751508 -0.35170258921 -0.4450843247
## 1266 -0.0086120779 0.0555136740 -0.1881705373 0.30003919859 -0.3856549562
## 1267 -0.0086120779 0.0555136740 -0.1881705373 0.30003919859 -0.3856549562
## 1268 -0.1075834544 -0.4578893513 0.8528666579 0.65522734245 -0.3090661498
## 1269 -0.6991785520 0.9946308929 -0.9595794558 -0.64688476969 0.1115159048
## 1270 0.3153962563 -0.9510804943 1.2161978451 0.42403139731 0.1149038112
## 1271 0.5974680463 -0.0445033888 0.5605380360 -0.37418162179 -0.1279970718
## 1272 -0.8168304480 -0.5652730058 0.0862089994 -0.07116189156 -0.1355775651
## 1273 -0.2241480199 -0.0074110813 -0.5636584468 -0.19631552492 -0.7149481155
## 1274 -1.6805397084 -1.1204204163 -0.6960765631 0.55887227153 -1.3668378870
## 1275 -0.2009552737 -1.3518228874 -0.2367689491 0.18575676792 -0.5923732739
## 1276 -0.6933041055 -0.6166537112 0.1396612642 0.33815903970 0.8109046595
## 1277 2.2396574011 -0.6042525108 -1.0199131806 0.81474161916 0.2233003480
## 1278 0.3117473876 -0.0484325062 -0.3641463769 0.40897340399 -0.1483205865
## 1279 -0.6933041055 -0.6166537112 0.1396612642 0.33815903970 0.8109046595
## 1280 -0.7256234956 -0.8192713310 -0.4278627805 0.41793353843 -0.1566832149
## 1281 -0.6507423424 0.2557263948 -0.1873526564 -0.10188563764 -0.5536321283
## 1282 -0.6507423424 0.2557263948 -0.1873526564 -0.10188563764 -0.5536321283
## 1283 0.5357473331 -0.4545789458 0.9185138019 0.44849796454 -0.3072320544
## 1284 -0.0871357870 -0.4503788741 0.2549077060 -0.04668969019 0.3012767613
## 1285 -0.2190476477 -0.0129787068 -0.6493175344 0.01606762434 -0.3096373552
## 1286 -1.1617763712 -0.5264960287 0.0032618473 -0.63508378836 -0.4551406114
## 1287 -0.2049420248 -0.4749104967 -1.3144009501 -0.29974254718 -0.1289705303
## 1288 0.4730843202 -2.3676596058 -0.1008242192 0.51275731271 -0.3869954845
## 1289 1.3549716495 1.3858423985 1.4067704253 1.48111552820 1.3302888526
## 1290 1.3549716495 1.3858423985 1.4067704253 1.48111552820 1.3302888526
## 1291 0.5479426814 -0.7802191609 0.3259518037 0.19621890611 -0.3180724344
## 1292 -0.1336586063 -0.2159233623 1.0851420500 0.37082045719 -0.5224638632
## 1293 0.8528999597 0.5312620456 -0.1964930287 -0.29079923472 0.0587239781
## 1294 0.0171284849 -0.2438423464 -0.0540699825 0.24700033446 -0.2851319229
## 1295 -0.1336586063 -0.2159233623 1.0851420500 0.37082045719 -0.5224638632
## 1296 -0.3837171320 -0.9890364503 -0.7780644385 0.59887503700 -2.0099202968
## 1297 -0.3837171320 -0.9890364503 -0.7780644385 0.59887503700 -2.0099202968
## 1298 0.1382247812 -0.9294461964 0.1450546211 0.31423760368 -0.3284464002
## 1299 0.1332039200 -0.9635368504 -0.0001287666 0.31118603946 -0.2314796782
## 1300 1.8261680506 -2.9587518503 1.9275123040 -1.24512628847 0.3009160501
## 1301 1.5096867078 0.4410651648 0.3245727947 -1.17100085341 0.0868998647
## 1302 1.1610581923 -0.1286546003 1.4480897400 -0.90896136548 0.2441133551
## 1303 0.1415787936 0.3944827386 0.2356105421 0.51405844158 0.1282032259
## 1304 -0.8876637731 0.0681488679 -0.3999881794 -0.63114088796 -0.0964165255
## 1305 -1.1054777020 -1.5900743525 -0.2545243781 0.56171646304 0.0025440677
## 1306 -1.1166529640 -0.4268329443 -0.0478522743 -0.12856779925 0.3934121026
## 1307 -0.3915747379 -0.4576280605 0.8341354280 -0.50135838214 0.4502288354
## 1308 1.1333342925 -0.5651491945 -0.2174873923 0.27077519767 -0.2452667096
## 1309 -0.3915747379 -0.4576280605 0.8341354280 -0.50135838214 0.4502288354
## 1310 -1.3147359624 0.0655114177 0.0617182538 0.45100711821 -0.4534950644
## 1311 -1.1166529640 -0.4268329443 -0.0478522743 -0.12856779925 0.3934121026
## 1312 0.8120891015 -1.2919360759 -0.1560967155 0.55143597483 -0.1535417155
## 1313 0.2464296349 -2.0342035040 1.4459304336 -0.56031448069 0.3954627247
## 1314 -0.6506898762 0.3335296899 -1.0752501413 -0.15357488548 0.0744936761
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## 1316 -0.9040416884 -0.5808342626 -0.0333634192 0.17832180440 0.8557580468
## 1317 0.9988217951 0.7705332175 1.0297995112 -1.79334204888 0.7571292683
## 1318 0.0006213893 -0.9030891864 0.3984735667 -0.18388952802 0.2030244464
## 1319 -0.9040416884 -0.5808342626 -0.0333634192 0.17832180440 0.8557580468
## 1320 0.9763239550 -2.3543283350 -0.4164289780 -0.38255517458 0.1337195522
## 1321 -0.1721140388 -0.4381270600 0.6521639143 -0.16724764361 0.9870047211
## 1322 1.0178014830 0.7938469373 0.9493381712 -1.75254121655 0.8017036909
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## 1331 -0.8556847711 -0.4495133481 -0.2060134508 0.08907902239 0.8508644937
## 1332 0.0734703587 -1.2045096566 -0.1787470847 -0.23428080285 -0.3988733492
## 1333 -1.3293608306 -0.6510370210 -1.1473851646 1.04705240790 -0.4787369319
## 1334 -0.5159270674 -0.8607176376 0.4306223345 0.18862731632 0.1024495394
## 1335 0.8197695400 -0.9403174320 -0.4453037444 -0.41541303221 -0.2239447259
## 1336 0.6470135146 -0.8726345387 0.5615148830 0.35810752887 -0.4937763445
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## 1344 -0.3739810689 -0.1195115446 -0.3616573476 0.19169538879 -0.4793462898
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## 1347 0.6428482104 0.1566980438 -0.1059704513 0.12460053414 0.0970515543
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## 1352 0.1113629951 -1.3889792894 0.0520265936 0.64635157853 0.1522410247
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## 1356 -0.7579691347 0.0426891403 -1.9106696122 0.09013531005 0.4765416819
## 1357 -0.7017462456 0.0249013997 -1.8578497177 0.11376980277 0.4844391247
## 1358 0.1706906497 0.8263223861 -0.1637325667 -1.24082360958 -0.0317213601
## 1359 0.7030085533 -0.9743852511 -1.1211160589 -0.56528561721 -1.5494105203
## 1360 -1.0891667063 -0.6764749732 0.3227641660 0.25337562179 -0.1400896153
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## 1362 0.2975004489 -0.6504329621 0.6224980558 -0.31093746972 0.2937750930
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## 1365 0.5247756720 -0.6131320273 0.0473138604 -0.41229887925 -0.1611251453
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## 1367 -0.5582193645 -0.9639688096 -0.1271148900 0.42835054485 0.1132215151
## 1368 0.2661222189 0.8462827604 1.3274844781 1.47153548412 1.8006063836
## 1369 -0.6666444077 -0.9212731755 0.2353308700 -0.23368095929 0.7414955818
## 1370 -0.4708093533 -0.8501435971 -0.8735954271 0.46746492181 -0.2494025948
## 1371 1.1226211301 -2.4547531570 -0.8158045472 -0.61095058824 0.3111503465
## 1372 1.5778686512 -0.8154677152 0.1366091964 0.51779698554 0.6115274593
## 1373 1.1226211301 -2.4547531570 -0.8158045472 -0.61095058824 0.3111503465
## 1374 -0.2685044289 -1.1654146665 -0.4920220616 -0.22414994970 -0.2762699438
## 1375 0.3090983917 -2.0300639646 -1.2533742940 -1.07965063527 -0.8907976813
## 1376 -1.0443593577 -0.6726414075 -0.4706508971 0.22762072618 1.2032199728
## 1377 -0.8265577360 -2.1141926072 0.3026030498 0.57822164444 0.1538745689
## 1378 0.8045066620 0.5203538311 -0.1672833356 -1.55173380949 0.7303687135
## 1379 -0.5814805345 0.0727104314 -0.9387947789 0.38384172179 -0.1667999052
## 1380 0.3545594116 -0.1452366822 -0.0035766139 0.54271573652 -0.2159605465
## 1381 0.3545594116 -0.1452366822 -0.0035766139 0.54271573652 -0.2159605465
## 1382 -0.1786565791 -0.6026941682 -0.1605020731 0.17374464846 0.4512313721
## 1383 -1.1498140620 -0.6618810648 -0.0661363043 -0.22804298474 0.4623741611
## 1384 -1.1498140620 -0.6618810648 -0.0661363043 -0.22804298474 0.4623741611
## 1385 -1.0185550753 -1.2796077728 -0.1459839279 0.27747850165 0.3010483761
## 1386 -0.7183761902 -1.2575955459 -0.0178080993 0.05900053687 -0.3566027490
## 1387 -0.2464521229 -0.1690516726 -0.3622555964 0.44006980540 -0.1827815113
## 1388 -0.2464521229 -0.1690516726 -0.3622555964 0.44006980540 -0.1827815113
## 1389 2.2427684016 0.0230716505 -0.7580409750 0.89394453639 0.9745444041
## 1390 -1.7637332931 -0.9555343112 -0.5267897582 1.12363718066 -0.6183517193
## 1391 0.7638747390 -0.2341335836 -0.1214289569 -0.18287667665 -0.2039309966
## 1392 0.5218011827 -0.5194103368 0.0394123022 -0.21716888681 0.4437447581
## 1393 -1.0012610917 -0.9752696727 -0.5976608779 1.12414728031 -0.2873628939
## 1394 -0.8320248468 -0.4161477407 -0.4174071909 -0.24833531552 -0.0087479583
## 1395 -0.1805997363 0.0648294384 -0.9830560105 0.48152131511 -0.1842101844
## 1396 -0.0461845613 0.2325020645 0.3071423589 0.30683264315 -0.0776816552
## 1397 -0.0820723252 0.2680666609 0.3214898375 0.29604675730 -0.1700497665
## 1398 -1.0830882090 -0.6757342582 -0.2800762084 0.08079418003 1.0717736804
## 1399 0.3910237690 0.7442610863 -0.0966692990 -0.37688913223 -0.0186365847
## 1400 0.6702184800 0.0982420707 0.7245122919 0.50224833585 -0.1747077202
## 1401 -1.4514580561 -1.1073511658 0.4300871548 -0.15670254935 0.6326598268
## 1402 -1.4514580561 -1.1073511658 0.4300871548 -0.15670254935 0.6326598268
## 1403 0.4356564441 1.0463095820 0.1736393804 -0.13640110140 0.6385025128
## 1404 -0.1111155167 1.5288725538 0.2512808313 1.79435573376 0.4315993163
## 1405 0.4029018314 0.6556257584 -0.0636001502 -0.85117077630 0.5752110464
## 1406 -0.2148784597 0.6537484414 0.1839541262 0.81031453322 -0.3076908533
## 1407 1.5443930162 1.1092451284 -0.4695130496 1.65689446220 -0.0004678181
## 1408 0.1058120440 1.3705916578 0.8748418657 0.31963793874 -1.2053191714
## 1409 -0.1216594745 0.4124524679 1.1831670587 0.55592109478 -0.8957916954
## 1410 0.1058120440 1.3705916578 0.8748418657 0.31963793874 -1.2053191714
## 1411 0.9109119371 -1.6285137332 0.7011944617 -0.48169888269 0.2142349963
## 1412 0.6770568067 1.2639157079 -0.3918665445 -0.52090220862 0.7423345263
## 1413 1.5443930162 1.1092451284 -0.4695130496 1.65689446220 -0.0004678181
## 1414 -0.7298913481 -0.8069455172 0.9684956284 1.36426521870 0.5829613249
## 1415 -0.3316749442 1.1857709122 -0.2449044754 0.04803567803 0.4509759854
## 1416 0.2778251654 1.9533700193 0.2702366935 0.55121567889 0.1380660567
## 1417 -0.3316749442 1.1857709122 -0.2449044754 0.04803567803 0.4509759854
## 1418 0.2838158133 0.1625355476 0.1034446389 0.09554105657 -0.2936790947
## 1419 -0.9312556195 -0.7918098940 -0.7995060746 1.13390743419 -0.3253734175
## 1420 -1.4927761231 -1.1247473569 0.0005577091 -0.18460682842 0.5498605379
## 1421 -0.9312556195 -0.7918098940 -0.7995060746 1.13390743419 -0.3253734175
## 1422 -1.2346392716 0.6425005883 -0.6514364434 -0.00924936662 -0.3966317212
## 1423 0.0824064752 0.3179800664 1.0464778897 0.23835447031 -1.4771196295
## 1424 1.7116678292 0.4365711311 -0.6625813908 -0.19197650916 -0.2063717070
## 1425 -0.9781201064 1.1176903661 -0.7773365998 0.27691094330 0.0728701757
## 1426 -0.9781201064 1.1176903661 -0.7773365998 0.27691094330 0.0728701757
## 1427 -0.3431877209 0.8875988012 0.2311171012 -0.60416080539 0.0232571708
## 1428 0.1568454120 -0.2557928172 -0.6860592272 0.09584356794 0.2559422407
## 1429 -0.2915953443 -0.4050526647 0.8318629472 0.19069394431 -0.8992990037
## 1430 -0.6750823103 1.2056383170 0.4095269803 0.19189307047 -0.7416130364
## 1431 -0.6747932063 0.3840946751 -0.3747142014 0.02168717631 -0.0436677312
## 1432 -1.0610733161 -0.1877724257 -0.7418525692 0.31133520226 0.7349474450
## 1433 0.5724626586 0.9790514042 0.4197416822 1.03386694683 0.1622002048
## 1434 -0.1907059767 1.0939025557 -0.5633246435 0.57260062240 0.0667871453
## 1435 6.3451285203 0.1417984309 -2.2484308338 0.78286897229 -2.0078468050
## 1436 6.3451285203 0.1417984309 -2.2484308338 0.78286897229 -2.0078468050
## 1437 -1.5408916570 0.1282037040 -0.7846400282 -0.67118864220 -0.2967162936
## 1438 2.0491791436 -0.7016833105 0.3220829617 -0.49751654630 0.6177279463
## 1439 -0.1273042786 0.2298054843 -0.3201843735 0.29720164948 -0.1054806617
## 1440 -0.3315606933 -0.5986703645 1.0033071274 0.32560729374 -0.3297107791
## 1441 -0.3558795586 -0.1653044645 -0.2117849075 0.70771688365 -0.1810544176
## 1442 0.7063095539 -1.3045371463 -0.4109716463 0.99308825444 0.6516969378
## 1443 0.0679158417 0.8881669298 0.4370456981 0.43072958770 -0.5131885820
## 1444 -0.1042712595 -1.0124327609 -0.2131114115 0.01930258308 0.1793038914
## 1445 -0.3205916250 -0.6099823673 1.0135558176 0.31880011899 -0.3308869778
## 1446 0.7172786222 -1.3158491492 -0.4007229562 0.98628107969 0.6505207391
## 1447 0.0679158417 0.8881669298 0.4370456981 0.43072958770 -0.5131885820
## 1448 -0.0391060636 0.7530011106 0.5343836390 0.38608445199 -0.5967856767
## 1449 -0.5604239499 0.8525438654 0.4815065656 0.05310564332 -1.4781243975
## 1450 -0.3371881730 -0.0784335904 -0.0454917379 0.83255203133 -0.1511212468
## 1451 -0.3558795586 -0.1653044645 -0.2117849075 0.70771688365 -0.1810544176
## 1452 0.6421242918 0.7074247032 -0.5966248188 -0.12704081688 0.1828294804
## 1453 -0.2177188681 -0.4073149156 0.8766178464 0.53204955462 -1.2959473049
## 1454 -1.2369109354 0.7788240760 -0.4174676127 -0.53495089983 0.3488568774
## 1455 -0.0480293160 0.2324326651 0.7561937467 -0.84725056204 0.2743490557
## 1456 0.5270223099 -0.2941672385 1.1177627382 0.15068053018 0.1181337498
## 1457 -1.5691359116 -1.0415640756 -0.2776290493 0.57964321913 -0.6545461638
## 1458 -1.2369109354 0.7788240760 -0.4174676127 -0.53495089983 0.3488568774
## 1459 -0.5521931804 0.7177391769 -0.7734536160 -0.37814882599 0.1978902252
## 1460 -0.1571004909 0.5124905182 -0.2222559889 0.51905861176 0.0516319779
## 1461 -0.4458556948 1.0942470007 0.0663326169 0.08321803330 -0.8128306644
## 1462 0.7720988151 0.9040588592 0.3668515984 -0.10342946648 0.2583197266
## 1463 0.0904867710 -0.2039726860 -0.0809250485 0.46276417627 -0.3141461564
## 1464 0.0937792209 -0.5862917028 -0.1185327379 0.42372712040 -0.2810992458
## 1465 -0.8137413147 0.6016492769 0.2979548508 0.04938730297 -0.8942701608
## 1466 -0.8137413147 0.6016492769 0.2979548508 0.04938730297 -0.8942701608
## 1467 -0.7920105626 0.5189666218 0.0015372142 -0.12067461618 -0.5512813591
## 1468 0.9911829306 -0.5523994145 1.4628990182 -0.69351891425 -0.0455549400
## 1469 -0.7920105626 0.5189666218 0.0015372142 -0.12067461618 -0.5512813591
## 1470 -0.1379390192 -0.6579943806 1.1337939894 0.23570293595 -0.1493522462
## 1471 -1.4872029836 -2.1045730705 0.1424255716 1.41650383926 -0.7943888410
## 1472 1.6469446272 -1.3806901003 0.0440023525 0.25853889953 -0.0552412982
## 1473 0.0846714009 0.7052646705 -0.1287784205 -0.57786781490 0.2513249651
## 1474 -0.3106292561 -0.7629409316 -0.3002689829 0.36823318735 0.1067842385
## 1475 5.3550220323 0.0552720648 -2.4450357673 0.66699635800 -1.5352721725
## 1476 0.7657073375 -0.3575241570 0.9548236668 0.53535668188 1.1009833204
## 1477 5.3550220323 0.0552720648 -2.4450357673 0.66699635800 -1.5352721725
## 1478 0.7766764057 -0.3688361599 0.9650723569 0.52854950713 1.0998071216
## 1479 2.5842507844 -0.2634118626 -0.2337686987 0.59493678616 0.1895161893
## 1480 0.5366469605 1.0030560888 -1.0378424687 -0.91194568963 0.4239600564
## 1481 0.4125515655 1.0802971813 -0.3770291978 -0.34953802499 0.3150372956
## 1482 0.5366469605 1.0030560888 -1.0378424687 -0.91194568963 0.4239600564
## 1483 0.3080086423 -0.1697865117 -0.8113237130 -0.78888063265 0.7260667132
## 1484 -0.6923189207 0.5210460436 -0.5170404068 -0.05701133688 0.3358083540
## 1485 1.0425748955 -0.0205592036 1.1013762897 -0.12722740138 0.4063866370
## 1486 -0.1192387506 0.0640472191 -0.3389370412 0.22584438306 0.2073852092
## 1487 0.1146058189 -0.1457989101 -0.0308952159 0.17561304915 0.2718203307
## 1488 0.6072408297 0.7804967179 0.3762672378 0.46004812132 0.1303508729
## 1489 0.6820554417 0.9628140905 -0.1958890357 -0.50965263981 0.4240569568
## 1490 0.4840168119 0.5565279813 0.1606934698 0.91373021061 0.2971411979
## 1491 -0.3279175909 -0.1634732310 -0.8536617482 -0.30478638597 0.4458367711
## 1492 0.6820554417 0.9628140905 -0.1958890357 -0.50965263981 0.4240569568
## 1493 0.4940414164 -0.2875436861 0.2540343434 -0.26755745823 0.3148395247
## 1494 -1.5139081457 -0.5435617309 -0.0425456831 0.08223026502 1.3836398067
## 1495 -0.7603726020 1.0898643731 -0.7926757080 0.62573503703 -0.3486132749
## 1496 -0.0379988655 0.5716643059 0.0036258273 0.98081325270 -0.5647810828
## 1497 -1.5139081457 -0.5435617309 -0.0425456831 0.08223026502 1.3836398067
## 1498 0.8596560775 -0.2841531394 0.7332354216 0.30171899216 0.0319616927
## 1499 1.0570534271 -0.1554955236 0.7434233730 -0.21542743244 0.1958999607
## 1500 0.8596560775 -0.2841531394 0.7332354216 0.30171899216 0.0319616927
## 1501 -0.5865804109 -0.6198121699 -0.0752152272 0.72847289961 -0.3264857755
## 1502 0.2739546843 -1.2660275055 -0.1717213869 0.72243322029 0.1426259079
## 1503 -0.4492299680 -0.1647398536 -0.3902376077 0.23904681047 0.2806872553
## 1504 0.7297463417 0.7735909179 -0.6789678105 -0.34911635287 0.6661910688
## 1505 0.3441018544 -0.2565108126 -1.1234921766 -0.95726112868 0.5569477707
## 1506 0.5447024485 0.6174637780 0.2725133894 -0.10212521590 0.1369735056
## 1507 0.8592087926 0.0874872477 0.7525438202 -0.00175468499 0.2155317200
## 1508 0.3441018544 -0.2565108126 -1.1234921766 -0.95726112868 0.5569477707
## 1509 -0.1502992804 1.3395877361 -0.6893860323 -0.75197967516 0.2483279550
## 1510 -0.6423791163 0.2764566386 -0.9381122380 0.54310389543 0.1612633485
## 1511 -0.4727328233 1.8059172947 -0.2613000686 0.25977449999 -0.2782621891
## 1512 0.1449313215 0.7582901281 0.4806937097 0.12000001591 -0.5292917132
## 1513 0.7284555594 0.4405896807 0.5891183966 0.03578216945 0.0261482329
## 1514 -0.7451645896 -0.7891475844 -0.4031887184 0.58907222099 0.3313739172
## 1515 1.2895110523 0.9731010085 0.5005838004 -0.06032128776 0.8961374835
## 1516 1.2949955865 0.9674450071 0.5057081454 -0.06372487514 0.8955493842
## 1517 0.0890845292 1.6141973572 0.0942400380 0.60969289175 0.0954367856
## 1518 -0.2850398641 0.6576451108 0.4569845382 0.79587737503 -0.8309866847
## 1519 0.1734226849 -0.0597715373 -0.8471443036 -0.43714940893 0.4329393302
## 1520 0.6761180184 0.0447356285 -0.2266710912 -0.21595215808 -0.2168172088
## 1521 -0.2850398641 0.6576451108 0.4569845382 0.79587737503 -0.8309866847
## 1522 -0.7606258919 0.3973630476 -1.4509688412 -0.06699870567 0.0617203309
## 1523 0.0890845292 1.6141973572 0.0942400380 0.60969289175 0.0954367856
## 1524 -0.7732503892 -0.0688967146 -0.3344055510 -0.10494080642 -0.3838621747
## 1525 -0.4062914553 0.8852672897 -0.3985996571 0.56366488167 -0.1485830556
## 1526 -0.5543968061 0.5082290559 -0.4795038321 0.78655428316 -0.4703588205
## 1527 -0.6172110017 0.4623624683 -0.5067516861 0.95467658622 -0.4429802591
## 1528 -0.2826961600 -0.1709326220 0.0674212212 0.67616526716 -0.5814301701
## 1529 -0.6901125136 0.6182600010 -0.8490899719 -0.46055108400 0.2099920659
## 1530 -0.4951975963 0.1271532063 0.6669358608 -0.38723593886 -1.4348107370
## 1531 0.3495495039 0.3096551769 -0.2418603693 0.58414172563 -0.1375329242
## 1532 1.0369137141 0.8550215390 0.0305753764 -0.31549596143 0.2236120530
## 1533 -0.6056040889 0.0981361519 -0.0567821117 1.09125015757 -0.7193628418
## 1534 -1.4238506263 1.0294844106 -0.4800752916 -0.44700225925 -0.1609012256
## 1535 0.4541445518 -0.4282125500 0.0113008867 0.22064574802 -0.0345720643
## 1536 -0.3327744670 0.2970076231 -0.4681444117 0.35764583110 -0.1705207874
## 1537 0.0323637110 1.0633901948 -0.1016862629 -0.41198552427 -0.2750023256
## 1538 -0.1082122801 0.6729363938 -0.2286066604 0.19558408861 -0.2414502841
## 1539 1.2060368665 0.0510223607 0.3261455018 -0.58875161396 -0.1061023291
## 1540 -0.7175047936 1.5402373004 0.1277495476 -0.46903660634 -0.4610897927
## 1541 1.9579193737 0.6242970983 -0.3936830268 0.02627916175 0.1436832789
## 1542 -0.2777876073 1.2569436842 -0.2259535010 0.11669392098 -0.0080963869
## 1543 -0.2562245543 -1.1304269297 0.8805105153 0.30155300042 -1.0009972438
## 1544 -0.5301821265 0.0369279024 0.1977841799 -0.48052137554 -0.5096413959
## 1545 -0.1420149883 -0.0814270353 0.0841789651 0.79587098828 -0.1260105752
## 1546 -0.3816674029 0.0730633735 -0.0372900226 0.10212843482 0.1973288632
## 1547 -0.1856878976 0.2009077688 -0.0944703063 0.45898671657 -0.6559324804
## 1548 0.2000797517 -0.4780215094 0.1345773063 -0.40191188612 0.0974862061
## 1549 -1.0399036489 -0.6796538766 -0.0103257809 -0.48027749077 -0.3827118352
## 1550 -0.4481584851 -0.3715020757 -0.3257262104 0.49700782338 -0.4679206105
## 1551 0.1763724840 0.6350731557 -0.1394090005 -0.23558284790 -0.5919013934
## 1552 0.0718399507 0.6726334566 -0.1432672754 -0.12194374772 -0.6346413763
## 1553 1.3101698909 -0.0939065921 0.2550649483 -0.32321617588 -0.8676033760
## 1554 0.0928503113 0.5576402278 0.2885816285 0.07661651136 -0.5960367385
## 1555 0.9926149241 0.0523397462 0.9358410651 -0.33316711532 -0.1586521440
## 1556 -0.0951456962 0.4144303651 0.1337617515 0.06962700976 -0.1603976310
## 1557 0.5750287682 0.2454321337 0.5299987335 -0.23761083107 0.0315653631
## 1558 0.9926149241 0.0523397462 0.9358410651 -0.33316711532 -0.1586521440
## 1559 1.3828561514 -1.4719549031 -1.7343094764 -1.02982566141 -1.9475966123
## 1560 -1.5798775242 -0.9789720135 0.1061659333 -0.02668926945 0.5786033563
## 1561 -1.5798775242 -0.9789720135 0.1061659333 -0.02668926945 0.5786033563
## 1562 -1.5798775242 -0.9789720135 0.1061659333 -0.02668926945 0.5786033563
## 1563 -0.2096108885 -0.5653170356 -0.0986407009 0.39151214992 -0.2798771174
## 1564 -0.2096108885 -0.5653170356 -0.0986407009 0.39151214992 -0.2798771174
## 1565 -0.2096108885 -0.5653170356 -0.0986407009 0.39151214992 -0.2798771174
## 1566 0.0867271308 0.1669833568 1.0931125902 0.66189054933 -0.6929023383
## 1567 -0.7969095672 0.5712693755 -0.9839254598 -0.34151132744 0.3552332100
## 1568 -0.2096108885 -0.5653170356 -0.0986407009 0.39151214992 -0.2798771174
## 1569 -0.9878755565 0.4492640530 -0.3207448595 0.14415436807 -0.6546619780
## 1570 0.0169351826 -0.0826365956 -0.2002473832 -0.08503132688 0.0191945039
## 1571 1.2452598243 -1.0070955783 -1.0436107407 -1.04626951724 -0.0069394274
## 1572 -0.0250753317 0.7726172107 -0.3050473999 0.40125649009 -0.2326021544
## 1573 -1.2249665902 -0.4568780862 0.1336048443 -0.48896138937 0.2526351341
## 1574 1.0395938543 -0.0889524635 0.3804865146 -0.67707370850 0.5498357628
## 1575 5.0491620414 -0.1661931966 -3.9307720663 1.09268659672 1.5221511012
## 1576 0.0820714404 -0.7833167585 0.0952858681 0.15273286938 0.0757473471
## 1577 -0.5811131990 0.4898787555 -0.3842312628 -0.38354252863 0.2987551386
## 1578 2.5539187767 -0.1343245985 -0.0825489390 -0.14409246332 0.1434765374
## 1579 0.6621786072 -0.4425966214 0.1852543452 -0.39901801490 -0.2288719242
## 1580 -0.0667876847 0.2177368028 0.1682280185 -0.32589907861 -0.5352837169
## 1581 0.2005614907 -0.2434379357 -0.5763771508 -0.04994373877 0.1892768144
## 1582 -0.0667876847 0.2177368028 0.1682280185 -0.32589907861 -0.5352837169
## 1583 0.8756566949 -0.5715380326 0.1715732614 -0.40132620333 0.1619815912
## 1584 -1.1838172695 0.1721487013 -0.4924109933 0.02890857753 0.2467899965
## 1585 -0.0966951379 0.2031307799 -0.1841336592 0.32560138171 -0.2877143531
## 1586 0.0769502175 0.0915977378 0.1475914476 0.35544910884 0.0947057092
## 1587 -0.3507442855 0.4359913498 0.2031104107 0.05485759398 -0.5140111232
## 1588 0.2787097284 0.5124009535 0.1349765056 -0.23279701763 -0.1175611568
## 1589 0.0798678511 -1.5500427043 1.4685202624 0.02213737747 0.2225051687
## 1590 2.0631587340 -0.2591573149 -1.0568001888 1.16128138201 -0.0416663853
## 1591 -0.0373561161 -0.4883482183 0.5050664520 0.69026967692 -0.7251568229
## 1592 0.8125703153 -0.1200113277 0.1620708793 -1.06579366441 0.0933433859
## 1593 0.1936550525 0.5062511842 0.2310099426 0.07935736022 -0.8085204546
## 1594 -0.2377419372 1.1409062250 0.6082815168 0.43431872000 -0.7684468612
## 1595 -0.2408605114 -0.0548179672 -0.1707581855 -0.35575470726 -0.9712205553
## 1596 0.1195549986 0.6745004806 0.6077800833 -0.24756247172 -1.0578036441
## 1597 0.1936550525 0.5062511842 0.2310099426 0.07935736022 -0.8085204546
## 1598 0.0677143109 0.8601385373 0.3213864046 -0.46872925460 -0.6120565780
## 1599 0.4503412053 0.4959984757 -1.1887603713 0.04216248958 0.4041825367
## PC10 PC11
## 1 0.2676756897 -0.0486149089
## 2 -0.0628131998 0.1380986838
## 3 0.1873837439 0.1181916896
## 4 0.1303517471 -0.3166146403
## 5 0.2676756897 -0.0486149089
## 6 0.3714946631 -0.0366005927
## 7 0.3996626959 -0.1110056223
## 8 -0.0759700940 -0.3437010980
## 9 0.2371160663 -0.1169407049
## 10 0.0083102273 -0.0274530456
## 11 0.2137981086 0.0692063804
## 12 0.0083102273 -0.0274530456
## 13 0.1548857205 -0.4318746280
## 14 -1.0464607396 -0.1592945456
## 15 0.4940479170 -0.1016054092
## 16 0.4529442891 -0.1580198327
## 17 -0.1578775459 0.1651729349
## 18 0.5922294435 -0.2693064061
## 19 0.0470805215 -0.2965332688
## 20 0.2766398377 0.1504995264
## 21 -0.3074262215 -0.3959511721
## 22 0.1511264226 0.0674088959
## 23 0.0567602461 0.1173895518
## 24 0.8020813510 -0.0878551965
## 25 0.1071445141 0.0870404534
## 26 -0.1725905409 0.1559105930
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## 1386 -0.8685950389 -0.0008120595
## 1387 0.0442958537 -0.0248694568
## 1388 0.0442958537 -0.0248694568
## 1389 -0.6076902969 0.1349867607
## 1390 0.5763387767 0.3733363677
## 1391 0.5725758492 -0.2716506974
## 1392 0.1738794865 -0.0331606738
## 1393 -0.2121413592 0.0051084767
## 1394 0.0699983175 0.0235307720
## 1395 -0.3152821843 0.1738455362
## 1396 0.1113179431 -0.0816594840
## 1397 0.1018096607 -0.1090074789
## 1398 0.2098180648 0.0323659851
## 1399 -0.0349360748 -0.0526163995
## 1400 0.2458280929 -0.0155112223
## 1401 0.3316281239 -0.0407602162
## 1402 0.3316281239 -0.0407602162
## 1403 0.0047580718 0.3811924178
## 1404 -0.5968361827 0.2298229665
## 1405 0.0241048066 0.5033329105
## 1406 -0.0580438541 0.1403934441
## 1407 0.0010534215 0.2023215703
## 1408 -0.3062826045 -0.1195174186
## 1409 -0.2665089701 0.2901768402
## 1410 -0.3062826045 -0.1195174186
## 1411 -0.1487158751 -0.1971201340
## 1412 -0.7946230177 0.1475936998
## 1413 0.0010534215 0.2023215703
## 1414 0.5624422440 -0.3755532353
## 1415 -0.4127551617 0.3098763576
## 1416 -0.1338204288 -0.1218556661
## 1417 -0.4127551617 0.3098763576
## 1418 -0.0762746150 0.3734201817
## 1419 0.4849550346 -0.0698224239
## 1420 0.2831575318 -0.1643665417
## 1421 0.4849550346 -0.0698224239
## 1422 0.1253177637 0.0099078819
## 1423 0.2044358742 0.2436805064
## 1424 0.4192927175 0.2093569496
## 1425 -0.0092608865 0.2678201637
## 1426 -0.0092608865 0.2678201637
## 1427 0.5383887397 0.7792908428
## 1428 0.0169741940 0.2749579286
## 1429 0.4766164347 0.2484502984
## 1430 -0.2352275197 0.2404231211
## 1431 -0.0101464785 -0.0076282958
## 1432 0.0999016210 0.0087265707
## 1433 0.4047266667 0.1961655606
## 1434 -0.1624849291 0.1709791166
## 1435 -0.3209781144 -0.4747366808
## 1436 -0.3209781144 -0.4747366808
## 1437 0.6117436392 -0.0705636850
## 1438 -1.2006896143 -0.2324115287
## 1439 0.3138828216 0.1501218267
## 1440 0.4999252589 0.1570328774
## 1441 -0.3282933900 0.3577614819
## 1442 -0.1513998493 0.1106595048
## 1443 -0.0333221100 0.2622542318
## 1444 -0.2256176992 -0.1226172753
## 1445 0.5094033396 0.1668710268
## 1446 -0.1419217685 0.1204976542
## 1447 -0.0333221100 0.2622542318
## 1448 -0.0857012633 0.2313162102
## 1449 -0.1941570709 0.2065701012
## 1450 -0.3506302578 0.3523661658
## 1451 -0.3282933900 0.3577614819
## 1452 -0.1108154311 0.4893303776
## 1453 -0.1750043519 0.0449705086
## 1454 -0.3703746219 -0.1527957840
## 1455 -0.1125714355 0.2063294442
## 1456 -0.5239371129 -0.1335419893
## 1457 -0.2725472667 -0.2578235885
## 1458 -0.3703746219 -0.1527957840
## 1459 -0.0899729515 -0.1731955000
## 1460 0.1653393625 0.0634377285
## 1461 0.1995123588 0.6765490636
## 1462 -0.2874301929 0.0336782457
## 1463 0.0341221617 0.0650401933
## 1464 0.3371200373 0.1038389862
## 1465 -0.5408380012 -0.0132619930
## 1466 -0.5408380012 -0.0132619930
## 1467 -0.6159843108 0.6010597032
## 1468 -0.7821437964 0.2838649714
## 1469 -0.6159843108 0.6010597032
## 1470 -0.6233633709 0.3148853833
## 1471 0.1814899028 0.0072715936
## 1472 -0.0194493035 -0.1740350694
## 1473 -0.9714862734 0.3726189940
## 1474 -0.0390260068 -0.0973975825
## 1475 -0.8200941865 -0.4933797942
## 1476 0.6519882447 -0.0971572444
## 1477 -0.8200941865 -0.4933797942
## 1478 0.6614663254 -0.0873190950
## 1479 -0.2686737697 0.1566116219
## 1480 -0.4144182758 0.4405148289
## 1481 -0.1842478358 -0.3050562292
## 1482 -0.4144182758 0.4405148289
## 1483 -1.0093073991 0.6423683056
## 1484 -0.5176331584 -0.0279436981
## 1485 -0.4766028977 0.0101052465
## 1486 -0.4001703509 0.0159545856
## 1487 -0.6423964042 0.0750472705
## 1488 -0.3252406584 -0.0901691746
## 1489 0.1157661618 -0.2631962337
## 1490 -0.3549621038 -0.0628773094
## 1491 -0.3416591650 0.1594580759
## 1492 0.1157661618 -0.2631962337
## 1493 0.0376239980 -0.1780874644
## 1494 0.7210874551 -0.0586381349
## 1495 0.1214476409 -0.1811797688
## 1496 0.3521168557 -0.1241246398
## 1497 0.7210874551 -0.0586381349
## 1498 -0.0168091443 0.0418606284
## 1499 -0.4436636642 0.0039459504
## 1500 -0.0168091443 0.0418606284
## 1501 -0.3001505588 -0.1885594914
## 1502 -1.0513348999 -0.0563740934
## 1503 0.0204595331 0.0938151157
## 1504 -0.6764241737 0.2179937256
## 1505 -0.5340065862 0.4561917688
## 1506 -0.2484653409 -0.2485334802
## 1507 -0.2890356610 -0.0836731892
## 1508 -0.5340065862 0.4561917688
## 1509 -0.8085832910 0.9878095003
## 1510 -0.0916799052 -0.2017042090
## 1511 -0.4114313387 0.1707430808
## 1512 -0.2522388875 0.1361563167
## 1513 -0.4859665427 -0.0176631309
## 1514 -0.1900497150 -0.4046207134
## 1515 -0.7569690006 -0.0880090690
## 1516 -0.7522299602 -0.0830899943
## 1517 -0.1277286865 0.4300526111
## 1518 -0.3892444425 -0.0576474293
## 1519 -0.5533272966 0.3649162866
## 1520 -0.0967898183 0.2153076880
## 1521 -0.3892444425 -0.0576474293
## 1522 -0.3112395949 -0.1540983442
## 1523 -0.1277286865 0.4300526111
## 1524 -0.3685701110 0.1872115236
## 1525 -0.4048825057 0.1297417443
## 1526 -0.1437384846 0.0300477696
## 1527 -0.1063963268 0.0076283008
## 1528 0.1038781264 0.2077074887
## 1529 -0.2415880728 0.2911991330
## 1530 -0.6411662495 0.0955025480
## 1531 0.2048958555 -0.0233415095
## 1532 -0.4211373515 -0.0868275377
## 1533 -0.4416975772 0.2793227496
## 1534 -0.1503090022 -0.2177840699
## 1535 -0.0791387436 -0.0317841054
## 1536 -0.1929566921 -0.0366596554
## 1537 -0.1443969834 -0.2395311589
## 1538 -0.5578955231 0.1221793179
## 1539 0.1189567182 0.2570434204
## 1540 -0.5374087614 0.1989507935
## 1541 0.4102270393 0.2147466309
## 1542 0.2605227095 0.3173199248
## 1543 -0.6739435751 0.2434853957
## 1544 -0.0954100773 -0.4325247757
## 1545 -0.5366516522 0.2312285949
## 1546 -1.2110866226 0.2287514006
## 1547 0.0774175022 0.0754230421
## 1548 -0.0274322093 -0.2100909358
## 1549 0.1655202622 -0.0910546148
## 1550 -0.2551152603 0.1743440604
## 1551 0.1052475364 0.2267200474
## 1552 0.0398545238 0.1775516792
## 1553 0.0791475468 0.0920950520
## 1554 -0.1060372642 0.1501172996
## 1555 -0.3082174606 0.1733770828
## 1556 -0.3872383848 -0.1137851612
## 1557 -0.6295444739 -0.0656860409
## 1558 -0.3082174606 0.1733770828
## 1559 -0.6717817157 0.2421146895
## 1560 0.2761189037 0.1296707737
## 1561 0.2761189037 0.1296707737
## 1562 0.2761189037 0.1296707737
## 1563 -0.4546639999 0.0902736008
## 1564 -0.4546639999 0.0902736008
## 1565 -0.4546639999 0.0902736008
## 1566 -0.2950267102 0.0163011380
## 1567 -0.9326375764 0.4532805182
## 1568 -0.4546639999 0.0902736008
## 1569 -0.4172792289 0.0876049737
## 1570 -0.0502514637 0.0155869908
## 1571 -0.3201198194 0.0606353770
## 1572 0.0200911001 0.2358709377
## 1573 -0.7201850575 0.0476460167
## 1574 0.1372882421 0.2367245412
## 1575 -1.8873060823 -0.4116350454
## 1576 -0.6794423405 0.3830904984
## 1577 -0.9784532929 0.2433661203
## 1578 -0.2346187767 0.2339432201
## 1579 -0.0843867176 0.2244168989
## 1580 -0.2947573522 -0.1110887844
## 1581 0.0967033071 0.1474316895
## 1582 -0.2947573522 -0.1110887844
## 1583 -0.3239507020 -0.2022050471
## 1584 -0.3349628292 0.3302222749
## 1585 -0.7968940709 0.5359347815
## 1586 -0.6945523274 0.2425476842
## 1587 -0.5683584012 0.0557286227
## 1588 -0.4168900954 0.0723752448
## 1589 -0.2503424198 0.3899485324
## 1590 -0.9312215331 -0.0497980188
## 1591 -0.5487660642 0.0568514481
## 1592 -0.3672963489 -0.0688857915
## 1593 -0.2421726645 0.4027843262
## 1594 -0.6201664707 0.0199245906
## 1595 -0.3567394609 0.0533656577
## 1596 -0.4787288976 0.2411825169
## 1597 -0.2421726645 0.4027843262
## 1598 -0.7791600898 -0.0409097505
## 1599 -0.7791958634 0.4496398973******************4) Model Insights PCA (10 points)************
Now after aggregating:
Obervations:
[1] Quality = Bad is towards -ve in PC1, PC2, PC3. Having highest varaibility in PC3 and PC2
PC3: had correlation with variations Alcohol
refer to correlations mapped earlier to decode PCA and analyse to get meaning from variables.
[2] PC2: free sulphur dioxide (negative), Total Sulphur dioxide (negative) and alcohol (+ve) relation.
[3] PC3: Volatile acidity (-ve),alcohol has high relation (+ve), free sulphur dioxide (+ve)
# Means of scores for all the PC's classified by Quality status
tabmeansPC <- aggregate(dftyp_pca[,2:6],by=list(quality=df$quality),mean)
tabmeansPC## quality PC1 PC2 PC3 PC4 PC5
## 1 bad -0.1205425 -0.5539665 -0.4278955 -0.02839395 -0.1299321
## 2 good 0.1048931 0.4820480 0.3723441 0.02470772 0.1130637sorted indices to rearrange the rows of the data frame tabmeansPC, resulting in a new data frame where the rows are sorted based on the values in the “quality” column in descending order.
2- indicates quality = good 1- indicates quality = bad
[1] Good quality wine is displaying +ve correlation
[2] Good quality wine is displaying -ve correlation
[3] also PC2 and PC3 have highest varibility with PC2 and PC3 indicating high variability for good and bad wine indicating a good criteria to differentiate and understand relation of variables with dependent variable
Mapped values:
[1] Good quality wine is displaying +ve correlation
[2] Good quality wine is displaying -ve correlation
[3] also PC2 and PC3 have highest varibility with PC2 and PC3 indicating high variability for good and bad wine indicating a good criteria to differentiate and understand relation of variables with dependent variable
dftyp_pca[,2:11]: This part selects columns 2 to 11 (inclusive) from the data frame
dftyp_pca. It subsets the data frame to include only these specific columns.
by: This argument specifies the grouping variable or variables. In this case, it’s specified as list(quality = df$quality), which means that the data will be grouped by the values of the “quality” column from the data frame df.
sd (Standard deviation): This is the function that will be applied to each subset of the data. In this case, it’s sd, which calculates the standard deviation.
after calculating standard deviation, transposition is done
viewing std deviation of Dependent variable with respect to PC’s.
Observation:
Chosen PC’s: [1] PC1 to PC5 have values above 1 indicating high std deviation with respect to dependent variable
tabmeansPC <- tabmeansPC[rev(order(tabmeansPC$quality)),]
print(tabmeansPC)## quality PC1 PC2 PC3 PC4 PC5
## 2 good 0.1048931 0.4820480 0.3723441 0.02470772 0.1130637
## 1 bad -0.1205425 -0.5539665 -0.4278955 -0.02839395 -0.1299321tabfmeans <- t(tabmeansPC[,-1])
print(tabfmeans)## 2 1
## PC1 0.10489313 -0.12054251
## PC2 0.48204803 -0.55396649
## PC3 0.37234415 -0.42789549
## PC4 0.02470772 -0.02839395
## PC5 0.11306368 -0.12993206colnames(tabfmeans) <- t(as.vector(tabmeansPC[1]$quality))
print(tabfmeans)## good bad
## PC1 0.10489313 -0.12054251
## PC2 0.48204803 -0.55396649
## PC3 0.37234415 -0.42789549
## PC4 0.02470772 -0.02839395
## PC5 0.11306368 -0.12993206# Standard deviations of scores for all the PC's classified by Quality status
tabsdsPC <- aggregate(dftyp_pca[,2:11],by=list(quality=df$quality),sd)
#Transpose the resulting subset by transposition on rows and columns of PC's and Quality
tabfsds <- t(tabsdsPC[,-1])
colnames(tabfsds) <- t(as.vector(tabsdsPC[1]$quality))
tabfsds## bad good
## PC1 1.6712482 1.8290347
## PC2 1.3320046 1.2490520
## PC3 1.0934233 1.2501508
## PC4 1.2697679 0.9306623
## PC5 1.0013641 0.9461063
## PC6 0.8055277 0.8167378
## PC7 0.7659343 0.7568395
## PC8 0.6728227 0.6298228
## PC9 0.6231237 0.5415072
## PC10 0.4422690 0.4105856t-test of PC1 to PC5 with dependent varaible to obtain mean based on t-value and p-value
T-test: t-test of PC1 to PC5 with dependent varaible to obtain mean based on t-value and p-value:
Observations and Inferences: [1] PC1: The ratio of variances between the “bad” and “good” quality groups is not significantly different from 1
Inference (PC1): The variability in PC1 scores differs significantly between the “bad” and “good” quality groups, indicating that PC1 may capture important differences related to wine quality.
[2] PC2: The ratio of variances between the two quality groups is not significantly different from 1 (p-value = 0.06941).
[3] PC3: The ratio of variances between the two quality groups is significantly different from 1 (p-value = 0.0001727).
Inference (PC3): PC3 also exhibits significant differences in variability between the two quality groups, suggesting that PC3 may be informative for distinguishing between “bad” and “good” quality wines.
[4] PC4: The ratio of variances between the two quality groups is significantly different from 1 (p-value < 0.00000000000000022).
Inference (PC4):
PC4 shows substantial variability differences, further indicating its potential relevance for discriminating between wine qualities.
[5] PC5: The ratio of variances between the two quality groups is not significantly different from 1 (p-value = 0.1089).
Conclusion: In summary, PC1, PC3, and PC4 exhibit significant differences in variances between the “bad” and “good” quality groups, indicating potential heterogeneity in variances between the two groups for these components. PC2 and PC5 do not show significant differences in variances.
t.test(PC1~df$quality,data=dftyp_pca)##
## Welch Two Sample t-test
##
## data: PC1 by df$quality
## t = -2.5746, df = 1593.2, p-value = 0.01012
## alternative hypothesis: true difference in means between group bad and group good is not equal to 0
## 95 percent confidence interval:
## -0.39718151 -0.05368977
## sample estimates:
## mean in group bad mean in group good
## -0.1205425 0.1048931t.test(PC2~df$quality,data=dftyp_pca)##
## Welch Two Sample t-test
##
## data: PC2 by df$quality
## t = -15.968, df = 1533.8, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means between group bad and group good is not equal to 0
## 95 percent confidence interval:
## -1.1632778 -0.9087512
## sample estimates:
## mean in group bad mean in group good
## -0.5539665 0.4820480t.test(PC3~df$quality,data=dftyp_pca)##
## Welch Two Sample t-test
##
## data: PC3 by df$quality
## t = -13.654, df = 1597, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means between group bad and group good is not equal to 0
## 95 percent confidence interval:
## -0.9151961 -0.6852832
## sample estimates:
## mean in group bad mean in group good
## -0.4278955 0.3723441t.test(PC4~df$quality,data=dftyp_pca)##
## Welch Two Sample t-test
##
## data: PC4 by df$quality
## t = -0.94165, df = 1344.4, p-value = 0.3465
## alternative hypothesis: true difference in means between group bad and group good is not equal to 0
## 95 percent confidence interval:
## -0.16372836 0.05752503
## sample estimates:
## mean in group bad mean in group good
## -0.02839395 0.02470772t.test(PC5~df$quality,data=dftyp_pca)##
## Welch Two Sample t-test
##
## data: PC5 by df$quality
## t = -4.9656, df = 1538.1, p-value = 0.000000761
## alternative hypothesis: true difference in means between group bad and group good is not equal to 0
## 95 percent confidence interval:
## -0.3389829 -0.1470085
## sample estimates:
## mean in group bad mean in group good
## -0.1299321 0.1130637******************4) Model Insights PCA (10 points)************
Performing F-test to calculate Variance for Principle components w.r.t Dependent variable:
Observations:
PC1: The F-test result shows a p-value of 0.01129, indicating a significant difference in variances between the “bad” and “good” quality groups for PC1.
The ratio of variances is estimated to be approximately 0.8349, suggesting that the variance of PC1 scores is lower in the “good” quality group compared to the “bad” quality group.
PC2: With a p-value of 0.06941, the F-test result for PC2 does not show a significant difference in variances between the quality groups. The estimated ratio of variances is approximately 1.1372, indicating a slight increase in variance in the “good” quality group, although not statistically significant at conventional levels.
PC3: The F-test result yields a highly significant p-value of 0.0001727, indicating a substantial difference in variances between the quality groups for PC3. The estimated ratio of variances is approximately 0.765, suggesting a lower variance in the “good” quality group compared to the “bad” quality group.
PC4: Similar to PC3, the F-test result for PC4 indicates a highly significant difference in variances between the quality groups (p-value < 0.00000000000000022). The estimated ratio of variances is approximately 1.8615, suggesting a higher variance in the “good” quality group compared to the “bad” quality group.
PC5: The F-test result for PC5 yields a p-value of 0.1089, indicating no significant difference in variances between the quality groups. The estimated ratio of variances is approximately 1.1202, suggesting a slightly higher variance in the “good” quality group, although not statistically significant.
Conclusion:
In summary, PC1, PC3, and PC4 exhibit significant differences in variances between the “bad” and “good” quality groups, while PC2 and PC5 do not show significant differences. These results provide insights into the variability of principal component scores across different wine quality groups.
options(scipen=999)
## F ratio tests
var.test(PC1~df$quality,data=dftyp_pca)##
## F test to compare two variances
##
## data: PC1 by df$quality
## F = 0.83491, num df = 743, denom df = 854, p-value = 0.01129
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7266931 0.9598895
## sample estimates:
## ratio of variances
## 0.8349068var.test(PC2~df$quality,data=dftyp_pca)##
## F test to compare two variances
##
## data: PC2 by df$quality
## F = 1.1372, num df = 743, denom df = 854, p-value = 0.06941
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.9898364 1.3074757
## sample estimates:
## ratio of variances
## 1.137236var.test(PC3~df$quality,data=dftyp_pca)##
## F test to compare two variances
##
## data: PC3 by df$quality
## F = 0.76498, num df = 743, denom df = 854, p-value = 0.0001727
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6658324 0.8794985
## sample estimates:
## ratio of variances
## 0.7649832var.test(PC4~df$quality,data=dftyp_pca)##
## F test to compare two variances
##
## data: PC4 by df$quality
## F = 1.8615, num df = 743, denom df = 854, p-value < 0.00000000000000022
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.620233 2.140167
## sample estimates:
## ratio of variances
## 1.861506var.test(PC5~df$quality,data=dftyp_pca)##
## F test to compare two variances
##
## data: PC5 by df$quality
## F = 1.1202, num df = 743, denom df = 854, p-value = 0.1089
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.9750283 1.2879156
## sample estimates:
## ratio of variances
## 1.120222- Model Insights (10 points): PCA
Goal: Levene’s tests (one-sided): The one-sided p-value indicates the probability of observing a result as extreme as the one observed, assuming that the variances of the two groups are different in the direction of interest.
In summary, this code segment is useful for assessing whether there are significant differences in variances between the “bad” and “good” quality groups for each principal component, providing insights into the homogeneity of variances across different quality groups.
Observations and Inferences: Chosen PC’s: PC1, PC2, PC3, PC4, PC5
PC1:
The F value is 9.7234 with a corresponding p-value of 0.001852. The p-value is significant at the 0.01 level. Inference: There is evidence to suggest that the variances of PC1 scores between the two groups (“bad” and “good” quality) are significantly different.
PC2:
The F value is 3.0446 with a corresponding p-value of 0.0812. The p-value is not significant at the 0.05 level. Inference: There is no strong evidence to suggest that the variances of PC2 scores between the two groups are significantly different.
PC3:
The F value is 8.864 with a corresponding p-value of 0.002952. The p-value is significant at the 0.01 level. Inference: There is evidence to suggest that the variances of PC3 scores between the two groups are significantly different.
PC4:
The F value is 4.6593 with a corresponding p-value of 0.03103. The p-value is significant at the 0.05 level. Inference: There is evidence to suggest that the variances of PC4 scores between the two groups are significantly different.
PC5:
The F value is 2.518 with a corresponding p-value of 0.1128. The p-value is not significant at the 0.05 level. Inference: There is no strong evidence to suggest that the variances of PC5 scores between the two groups are significantly different.
# Levene's tests (one-sided)
library(car)
#leveneTest function takes the formula PC(1 to 5)~df$quality where PC1 represents the scores of PC1 to 5 and df$quality represents the quality groups.
#eg: (p_PC1_1sided <- LTPC1[[3]][1]/2 : extracts the p-value from the result of the Levene's test for PC1 and divides it by 2 to get a one-sided p-value.
(LTPC1 <- leveneTest(PC1~df$quality,data=dftyp_pca))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 9.7234 0.001852 **
## 1597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(p_PC1_1sided <- LTPC1[[3]][1]/2)## [1] 0.0009259385(LTPC2 <- leveneTest(PC2~df$quality,data=dftyp_pca))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 3.0446 0.0812 .
## 1597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(p_PC2_1sided=LTPC2[[3]][1]/2)## [1] 0.04059939(LTPC3 <- leveneTest(PC3~df$quality,data=dftyp_pca))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 8.864 0.002952 **
## 1597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(p_PC3_1sided <- LTPC3[[3]][1]/2)## [1] 0.001476128(LTPC4 <- leveneTest(PC4~df$quality,data=dftyp_pca))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 4.6593 0.03103 *
## 1597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(p_PC4_1sided <- LTPC4[[3]][1]/2)## [1] 0.015517(LTPC5 <- leveneTest(PC5~df$quality,data=dftyp_pca))## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 2.518 0.1128
## 1597(p_PC5_1sided <- LTPC5[[3]][1]/2)## [1] 0.05637501******************4) Model Insights PCA (10 points)************
Observations:
Quality= good
PC2 and PC3: seems to be correlated to Good quality wine, as green data points are densely populated towards positive value of PC2 and PC3 x-axis, y-axis
Quality = bad
seems to be uniformly distributed but skewed little towards negative scale of x-axis(PC2).
****Visualization ****
# Plotting the scores for the first and second components
plot(dftyp_pca$PC2, dftyp_pca$PC3,
pch = ifelse(dftyp_pca$quality == "good", 21, 2), # Circles with a border for "Good", triangles for "Bad"
bg = ifelse(dftyp_pca$quality == "good", "#006400", "red"), # Darker green for "Good" points
col = ifelse(dftyp_pca$quality == "good", "green", "red"), # Color assignment for the border
cex = 1, # Normal size for both "Good" and "Bad"
xlab = "PC2", ylab = "PC3", main = "Wine Quality against PC1 & PC2")
abline(h = 0)
abline(v = 0)
legend("bottomleft", legend = c("Good", "Bad"), pch = c(21, 2), col = c("green", "red"), pt.bg = c("#006400", NA))
******************4) Model Insights PCA (10 points)************
Observations: log(eigen value)
[1] Seems like log(Component varaince) drops below 0 after PC5 [2] At PC10 log seems to drop at -2
plot(log(eigen_df), xlab = "Component number",ylab = "log(Component variance)", type="l",main = "Log(eigenvalue) diagram")
print(summary(df_pca))## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.7604 1.3878 1.2452 1.1015 0.97943 0.81216 0.76406
## Proportion of Variance 0.2817 0.1751 0.1410 0.1103 0.08721 0.05996 0.05307
## Cumulative Proportion 0.2817 0.4568 0.5978 0.7081 0.79528 0.85525 0.90832
## PC8 PC9 PC10 PC11
## Standard deviation 0.65035 0.58706 0.42583 0.24405
## Proportion of Variance 0.03845 0.03133 0.01648 0.00541
## Cumulative Proportion 0.94677 0.97810 0.99459 1.00000Observations:
[1] PC1 to PC4 seem to have variance above one indicating meaning and should be visualized and decoded further
plot(df_pca, xlab = "Principal Component")
#get the original value of the data based on PCA
center <- df_pca$center #loading center values of PCA during transformation
scale <- df_pca$scale # retrieves the scaling values used during the PCA transformation.
new_df <- as.matrix(df[1:11])scale(new_df,center=center, scale=scale):
scales the original data new_df using the centering and scaling values obtained during PCA.
%*%df_pca$rotation[,1]:
performs matrix multiplication of the scaled data with the first principal component obtained from PCA, which essentially projects the data onto the first principal component.
From this we can further extract information and contribution of each attribute in respective principal components.
drop(scale(new_df,center=center, scale=scale)%*%df_pca$rotation[,1])## [1] -1.6190233808 -0.7989199991 -0.7482450102 2.3569352844 -1.6190233808
## [6] -1.5832119779 -1.1011195532 -2.2480043771 -1.0865466057 0.6545847432
## [11] -1.1248523852 0.6545847432 -3.0259143987 1.5384164486 1.5647785548
## [16] 1.6207417506 1.1741982777 2.6943618301 -0.7222123697 3.3342696553
## [21] 0.6234495719 -0.2059201160 0.6961895123 0.0530890262 -0.8782805799
## [26] -1.1642698338 -0.2393189176 0.6961895123 -1.7346751593 -1.3219581716
## [31] -1.5592690050 -1.8050008848 -0.0895459518 -0.0867786299 -1.0837568763
## [36] -0.5614524459 -0.8278540247 0.4339239895 -2.9857396410 0.7641366874
## [41] 0.7641366874 0.3507712392 1.4009534162 0.3617225771 -2.1413294956
## [46] -4.2775856693 0.2331430018 1.2327080334 -0.9389723339 -0.6576468843
## [51] 0.5882230958 -1.5606289219 -1.5208686045 0.8461291444 -0.2549027582
## [56] -0.7112083086 2.3003794236 0.3128443129 -0.7529628698 -0.5177435570
## [61] 0.5326550505 0.7033615032 -0.6972955953 -1.5574365312 -1.7058880733
## [66] -1.7058880733 -1.0600479370 -1.9571393728 1.4023857653 -0.7577244093
## [71] -0.9999191681 -0.7695600669 -0.8562065492 -0.1462766933 2.0689381719
## [76] 0.9132489307 0.9132489307 -2.1682947506 -1.6932037651 0.8949146807
## [81] -1.3094380643 4.2819216263 0.1196024010 2.0986279913 -0.2790824419
## [86] -1.2983952432 3.3203917603 -0.5115931700 2.5076218099 -1.3568807729
## [91] -0.1747656004 3.3203917603 3.4094800686 -0.5115931700 -4.6345864975
## [96] -4.0927389504 -1.8938782706 -0.3217304729 -1.5393328235 -0.2192307179
## [101] -0.2904842848 -0.4650346765 -0.2192307179 -0.3440831820 -0.7784454102
## [106] -0.3440831820 4.4437241847 -0.7903975135 1.1210572328 0.7757945360
## [111] 0.3891875737 -0.0669049221 -0.0201665498 1.6116220721 0.3891875737
## [116] 1.1698071038 -0.1289330701 -0.8434294325 -0.2852066931 -1.3645292367
## [121] -1.1256407134 -0.2852066931 -1.5075644741 -1.2698331012 -0.9868665687
## [126] 0.6533937651 -2.8296888090 -2.8856553229 -0.6487334280 -1.4346891605
## [131] 0.7289523106 -3.1805724630 -3.1805724630 -1.9396816470 -1.3976085562
## [136] -0.0815424656 -0.0882906684 -0.3093735873 -0.6507117783 -0.6374338891
## [141] -0.0815424656 -0.0882906684 -3.8605516273 -1.3641258354 -3.8605516273
## [146] 0.9475601351 -2.8652698825 1.3496627748 -1.4143329865 0.0462106670
## [151] -0.0523283441 8.6506175515 -1.5478779955 -1.5478779955 0.2712096264
## [156] 0.2641282213 0.2712096264 0.2641282213 -1.8498615530 -1.6111528310
## [161] -1.2859639597 -0.1120192706 -1.0742579809 0.0226183909 -0.0200148627
## [166] 0.4788210839 -1.5048751507 -1.5753352298 -1.9719192034 2.8717600321
## [171] -1.7369389890 -0.0779569541 -0.0779569541 -1.7779866591 -0.6624433130
## [176] -1.0754554515 -0.6624433130 -0.6169172941 -1.9660188094 0.0007361586
## [181] 0.0007361586 2.6388040315 -1.5905338598 -1.3679174303 -1.3220828649
## [186] 1.3202913800 0.3512441109 -1.0268162818 0.3302101255 0.2941554682
## [191] 0.6849249373 -1.1089200492 -0.5337173226 -0.5312619075 -0.5312619075
## [196] -0.0562276348 -0.8386778425 3.1677209277 -3.6115490683 -3.2161574621
## [201] 1.4045752635 2.4110640638 -1.0590735011 -0.3823451122 -0.3370745742
## [206] 3.7214910153 3.7214910153 -0.3599005233 0.4468686366 2.1823798659
## [211] 0.8345613544 -0.5724415472 2.3488234226 -0.7419871933 -0.3748185502
## [216] 0.6051692411 -0.0858886685 -0.7007545958 -1.0078732101 -0.0096018066
## [221] 0.0959528102 -0.0436767558 -1.9279513770 0.1546931895 0.0784825203
## [226] -0.5985753362 3.8407190827 -0.3415062675 -0.5985753362 -1.1369815155
## [231] -3.3203963427 -0.9127565249 0.4593750278 -1.1369815155 -1.2030535048
## [236] -1.2458929692 -1.2458929692 -1.2898703502 -1.2458929692 -1.2030535048
## [241] 2.7052732283 2.9507230143 -0.4067664560 4.3834559142 4.3834559142
## [246] -1.1971770943 -1.4173947547 -0.3856903866 -1.2329684667 -1.1971770943
## [251] 2.0070741656 -1.4713199674 2.2019356945 -0.0855690526 -1.4713199674
## [256] -0.0533098400 1.3084545104 -1.7824423313 5.1529261306 1.8926316087
## [261] -0.0308251162 -1.8610808098 -1.1787535779 -0.0206363712 3.1265999169
## [266] 2.2239393306 -1.0016620752 0.6745583099 -1.1449991099 3.3436547984
## [271] -0.7246593448 3.3436547984 2.7705825728 -0.2353655344 0.4274125078
## [276] -0.7246593448 -1.1449991099 3.3436547984 2.0178157279 1.2353152770
## [281] 2.9573317128 3.4026121669 -0.6281781983 1.2353152770 0.8561730111
## [286] 0.8561730111 3.0286257824 -0.4958104021 0.0532249793 3.5725192019
## [291] 0.0532249793 3.1371058691 1.2856424772 -0.5789261714 4.3735065308
## [296] 2.6545793026 1.0258089683 -1.4935260117 -1.2479747539 -1.6813440640
## [301] -1.1261189476 1.9380230911 -0.4495046383 -0.8925519301 1.1161123396
## [306] 2.1596261591 -0.4909436553 2.5740362637 2.6158522254 -0.2271865925
## [311] 2.1596261591 -0.3754289614 0.7790545576 0.3709803383 -0.3182426983
## [316] -0.5324568811 0.9857340146 0.0682013843 0.8065195761 0.0682013843
## [321] 0.8065195761 1.0913378105 -0.5786493035 1.5382273586 2.7692650017
## [326] 2.7692650017 3.0873061094 2.0063423101 3.8459792045 1.5748601207
## [331] 2.1147976115 2.1147976115 0.1785995334 -0.3865884068 -1.1525342309
## [336] 2.6709356731 -0.1732675216 0.1843473050 3.6999638933 4.3587223617
## [341] 3.8788860031 1.8071734923 1.9883293821 1.9883293821 2.9957675795
## [346] -1.7610389940 -2.4211980941 4.0617329780 1.6795446529 0.0815424640
## [351] 1.8206498952 0.0657245690 -0.5213494466 4.3839964082 -1.3511585954
## [356] -2.7049351652 2.7225920015 2.1054762294 3.6377038912 3.7402299853
## [361] 0.2954347898 0.8135790155 2.9760271811 3.6706958376 4.3892799173
## [366] 1.9808037810 4.3892799173 2.5916278305 2.8673289530 1.9119913874
## [371] -1.8490125711 0.7865851323 1.3362106119 0.3247468020 4.7876029568
## [376] 3.5932507026 3.0130182802 1.9119913874 3.2331876694 0.6693528537
## [381] 1.3554499227 4.5920898329 1.3554499227 1.3554499227 0.0047823430
## [386] -0.7391286994 -0.0154886172 -0.5647850001 0.0275182472 1.4388821018
## [391] -3.6346031116 4.5920898329 1.5943891646 1.1997505915 3.2669997767
## [396] 3.1753769295 -0.7935544314 2.3157778903 2.3157778903 0.0193242204
## [401] -0.7935544314 -0.6203987865 2.9954685936 2.2776909622 -0.6129961912
## [406] 0.6127886296 1.3366507824 3.1079036467 2.4571333684 2.8640470336
## [411] 0.6983753098 0.7179048121 -0.6883471565 2.3208606124 0.8290338411
## [416] 1.9658957174 1.7342678135 -1.7268536455 2.8272604901 -2.2384733786
## [421] 0.5104560598 -2.9379453288 -1.9078394555 2.3092629911 -1.9078394555
## [426] -2.9379453288 -2.6698811458 0.2005993874 0.6336576249 2.8248308132
## [431] 2.3092629911 0.2126650030 2.5647785520 3.2132819200 2.8499349677
## [436] 3.2132819200 -0.6253324578 2.6750164540 2.8499349677 -0.5519972372
## [441] 4.4104777611 3.0487796426 5.5353026325 1.0486353799 -3.1366432904
## [446] 0.1004276569 3.3902207995 0.8056683330 0.1608407480 3.2824688943
## [451] 3.2824688943 3.9212462064 -1.5119182559 2.5537422970 -0.8503685741
## [456] 2.3663151025 0.5148554636 0.4877010264 2.5537422970 2.7506550682
## [461] 0.8213194793 0.2756233733 2.2887459152 1.5422026457 3.3111852800
## [466] 1.9520019204 1.5025887554 -0.0783413905 3.2260222552 -1.2199010844
## [471] 3.7806147396 0.9663272335 3.2880736770 1.5885087723 2.4564707668
## [476] 0.9269953314 1.4100404940 2.4585284773 0.9269953314 0.4882581689
## [481] 4.4331012560 1.9260510625 3.7219672565 3.7566418720 2.4835011368
## [486] 0.7730634181 0.7730634181 0.7754386549 2.8427578376 1.1325865363
## [491] 0.2546396254 0.2742794474 0.3838318675 0.2609030033 -1.0259683220
## [496] 1.9453179863 -0.1323202712 0.0746504001 1.9453179863 0.2609030033
## [501] -0.1323202712 3.0740056348 3.0740056348 2.4001570896 2.2312431689
## [506] 1.8009790711 2.1319952494 2.5085956265 1.3462990796 3.9267610227
## [511] 3.0233474835 1.3462990796 2.8208511914 2.2003808022 2.2003808022
## [516] 2.6435464998 2.8782225235 2.6338559267 1.9552765317 0.3565170016
## [521] 1.7066772449 0.3170780339 1.2406867883 1.3771074539 1.3844493799
## [526] 1.3410589741 0.3565170016 0.0726602276 1.2351886623 0.9444469397
## [531] 1.0135709739 2.9374344615 2.9374344615 0.4731931599 1.2182102551
## [536] 1.0135709739 0.9444469397 -0.5203604485 3.4414459596 3.0257055388
## [541] 0.8966116493 2.0027718933 0.3167933675 1.8242062565 5.5003604339
## [546] 1.8234269211 -0.2214901369 2.1861450699 3.0835287410 1.3531187498
## [551] -1.4168217862 1.1676454978 1.3578562148 -4.0639695509 5.0903446976
## [556] 5.0903446976 2.7259424294 5.1184484969 2.7259424294 3.0619207947
## [561] 2.4566123568 1.2598392395 1.7436745235 0.6937115471 3.0619207947
## [566] 2.4566123568 0.9750974328 0.9750974328 2.3670218236 -1.9079911544
## [571] 2.6843706293 -1.9079911544 2.0098230436 1.4051419287 2.4094212911
## [576] 3.0386193461 0.4492157693 1.1698659635 1.1485427391 1.8136205644
## [581] 2.5053310512 2.5053310512 2.3099668814 2.9384057937 2.6409076451
## [586] -0.3281823776 3.0698464544 -0.4137554716 -3.5323125244 2.2877256712
## [591] 1.0588377991 -0.6458707802 1.0588377991 2.2448292655 -0.7072350554
## [596] 1.6987890950 3.0816260835 2.4441081104 -0.1876797616 3.2686798844
## [601] -0.7750688861 3.3677954563 -1.1296098743 3.3677954563 0.5167240728
## [606] -0.0421897513 1.1372793216 0.7136544372 2.1477209667 -1.9138056976
## [611] 1.1725698019 3.9229649729 -1.2215029021 0.3909134134 2.3414616671
## [616] 0.7541133639 0.7541133639 3.2315705622 3.0988015816 2.7495032393
## [621] 0.3238192283 0.2413929414 0.7892964765 -0.2895111666 -0.5074863403
## [626] -0.5074863403 0.3023997610 0.3023997610 0.4546272277 -0.1502794070
## [631] 0.4546272277 2.3302690054 -0.5638691680 0.4840736254 -0.1916634547
## [636] -1.4545428918 0.2829088144 0.1791229257 -0.9912326773 1.9299475446
## [641] 1.0728456949 0.6986170436 1.0728456949 0.6986170436 1.0728456949
## [646] 0.0516123430 -0.9522409339 -1.3571472887 -0.0148188811 0.0730832368
## [651] 2.5930748917 0.5235881412 4.7092275291 1.7120439632 1.2847153310
## [656] 1.4348163962 2.5930748917 3.0161878260 -0.5469547541 -1.5265409477
## [661] -0.5469547541 0.2240558964 -0.9086472195 2.1680971829 3.3047694277
## [666] 0.5128181265 1.4488820420 3.0618655706 0.6594202008 3.0618655706
## [671] -0.4756540111 -0.3553170343 0.1911368607 -0.3553170343 2.2961796834
## [676] 1.0806917507 2.2961796834 -0.9156804674 -1.1359334190 1.9501037512
## [681] 3.5076923844 -0.1468564617 0.2196113221 -0.9502950855 0.1618231077
## [686] -0.9502950855 -1.1278954647 0.7754448806 -1.7295760640 1.7667765702
## [691] -2.6796319383 -0.2411098372 4.0863559656 0.7945698336 0.7795840176
## [696] -4.7570170106 -1.5027212153 -1.5027212153 0.8850818655 2.6020749372
## [701] 1.2548205104 -1.5027212153 -1.5210472578 0.6280637866 -0.5386257098
## [706] -0.5060846684 -1.9090352061 -0.8258935560 -1.0439575229 1.4297631105
## [711] 1.3480066416 0.8701260759 -0.5155006970 0.1142581515 0.3555203142
## [716] -0.6215288318 0.1142581515 -0.2946467984 -0.4353694587 -1.3497063692
## [721] -0.4353694587 1.0417457870 -0.2622641931 2.2930946569 -2.3695525657
## [726] -0.1248637338 -0.5077890181 -1.5866951178 -1.5866951178 -3.2959842750
## [731] 3.3703914361 -0.6431042654 -1.7307727314 -0.4942352456 -0.4854639045
## [736] -1.1760580802 -1.1760580802 -0.4484857621 1.1452016678 -0.3011096966
## [741] -0.6914087775 0.6551926626 -1.6462958348 2.9949658078 2.7799871931
## [746] -0.8001888147 0.5931393943 0.7604462726 -0.8479286141 -0.8001888147
## [751] -0.2675412924 -0.2675412924 -0.4823718117 -0.2675412924 3.5828218360
## [756] -2.5882975604 -3.5691862362 -1.2300729390 -1.2300729390 0.7072286790
## [761] 0.5070425191 0.1443651950 -0.9823951857 0.1443651950 0.0936464036
## [766] 0.1080593870 0.4711701416 0.1243047088 -1.7064929338 -1.4126382690
## [771] -1.7064929338 0.3363144163 0.5643953017 1.0051199931 0.9771608383
## [776] -2.2290091065 -0.6615625670 -0.5629150936 0.2880646630 -1.3426486215
## [781] -0.6217883321 -1.4268600629 -0.7527717281 -1.4268600629 -1.6451886176
## [786] 1.4574000102 1.4574000102 1.0388595499 1.0388595499 0.6379601043
## [791] 0.4544341965 0.2440909587 -1.3965051923 -1.4997217522 1.0696326891
## [796] 2.4479889198 1.3981878268 0.6149311533 0.8463407705 0.8463407705
## [801] -0.7433761582 -0.2952003988 -3.3005506786 -0.1836637810 0.0231833536
## [806] -0.4695237098 -0.2087256144 -0.4695237098 0.3007698951 0.0764251040
## [811] -1.1890236274 3.2756178513 1.5053947146 -1.2836164505 2.9763061398
## [816] 1.5053947146 0.7583820815 0.9937439682 -1.5672663308 0.5372440416
## [821] -1.4655912406 -4.1758506384 -1.6118208200 -1.6118208200 -0.2592232220
## [826] -1.1079324675 -0.5875044985 -1.1079324675 -1.9100280725 -2.6245202017
## [831] -1.5349910966 -2.6245202017 1.6253595491 1.9932304384 -0.3583699613
## [836] -1.3870102749 -2.1434101500 -2.1434101500 1.1958045738 -1.8895107902
## [841] 1.7265331598 -1.6786061161 1.5435149849 -0.3123181549 1.4467584987
## [846] -1.6376925175 -1.6376925175 -1.0578238539 -1.6376925175 -1.6135692582
## [851] 0.8383283727 0.8383283727 1.3424325811 0.1138528986 0.1138528986
## [856] -2.0694525691 0.1138528986 0.0781816742 1.9813340287 -1.3404938162
## [861] -1.4262102968 -2.5204988910 -0.2362628364 -1.4856381662 -1.4262102968
## [866] -1.4525606224 -1.2267239901 -1.1761199099 -1.3404938162 -1.6876496610
## [871] -2.4403725399 -2.2862066951 -0.9810409090 0.4863840013 1.6310899129
## [876] 0.7840480431 -1.7760809302 -2.4403725399 0.2933908880 -1.5251891591
## [881] -0.0843722099 -2.1788399203 0.8638678490 -1.5251891591 0.2933908880
## [886] -0.2467583576 -0.2762469851 1.0736712984 -2.1907976053 2.5781750541
## [891] -0.9635339420 -1.6319396954 0.9900930101 -1.6319396954 -1.5874385541
## [896] -1.8579646941 -0.0358885314 -1.8579646941 -0.0358885314 -1.5030461838
## [901] -0.0445460132 -1.4960116480 -1.4960116480 -1.3139119145 -1.3139119145
## [906] 0.5730783709 -0.5902039533 -2.8613085572 -1.4755169741 -0.9405889574
## [911] 0.9249772244 2.5187721457 0.6159549852 1.0191453893 -0.9405889574
## [916] 0.2477249174 -2.6185074429 0.5453972996 0.2524504255 -1.0499103951
## [921] 1.0335398033 0.2524504255 -1.0499103951 0.5453972996 0.1363671106
## [926] 0.0653092792 0.7298473824 0.1439260476 0.1363671106 0.1331766935
## [931] -2.1339230114 -1.3733506754 -0.4469897279 -1.3733506754 -2.1339230114
## [936] -0.0006385448 -0.0006385448 2.1181114655 -0.8709115498 -2.5480099542
## [941] 0.5648128008 1.0679246591 1.5899546566 1.2334433723 0.5046874206
## [946] 1.2571827796 2.0792416335 0.1329178189 0.4530389182 0.4530389182
## [951] 0.4530389182 0.1329178189 0.0651340999 0.8107129267 -0.7745710086
## [956] 0.8437243245 0.7114707118 0.8447671694 -1.6683654481 -0.7810361720
## [961] 0.2169593784 -0.8188084292 -1.1410638547 0.8854650000 0.2169593784
## [966] 0.3022742374 0.8801810210 0.2374852901 0.1230599105 -1.3964723164
## [971] 1.5866049448 1.5866049448 1.3684382856 -0.2214779996 0.7830179587
## [976] -0.6043311386 -0.6043311386 0.7243532532 -0.8696541856 2.0654286326
## [981] 0.4130091449 -0.0026164093 -1.0392038117 0.4130091449 2.0654286326
## [986] -1.9649983508 0.4074557373 -0.6759148479 -0.2863638456 0.7950246409
## [991] -0.2863638456 -0.6889745007 -1.4422301438 -0.6889745007 1.9162762057
## [996] -1.0788223830 -3.6508563635 -3.6508563635 -0.1852979109 -3.6051215740
## [1001] -0.9397047159 1.0092364511 0.4493997617 -1.6153190699 -0.1567545890
## [1006] -1.6153190699 0.4493997617 0.4612273395 0.7755577952 1.1878309015
## [1011] -0.1064233368 0.5021854794 -1.1993248862 -1.5729317663 -0.7143899278
## [1016] 1.8569440847 0.1662851728 -0.5158032245 -0.5158032245 -1.5305735079
## [1021] 2.0564703173 2.0564703173 -1.4018563833 0.0474004970 -1.3434856892
## [1026] -0.7400140317 -1.1786805220 -3.1673972353 -0.8291878973 -1.3434856892
## [1031] -2.0693199544 -1.6079546499 -0.7452891991 -0.9721179817 -0.1323453435
## [1036] 1.2536645651 -0.5467211896 -1.4741802592 0.7246510092 0.1562830487
## [1041] -2.0820949410 -1.0940532504 0.1562830487 2.0225003397 -1.4851746237
## [1046] -1.9222880010 -1.5942850527 -1.2060379166 0.7409166582 0.7437478254
## [1051] -1.2060379166 4.2162276498 -3.4968403938 -0.2841906742 0.6040498059
## [1056] 0.6040498059 0.8997341230 0.7972954214 1.5575547700 0.8997341230
## [1061] 2.7951299384 0.4593969268 0.1638407106 1.5322471535 -1.6056656004
## [1066] -0.6127640740 -2.0731462052 2.3549073368 2.3549073368 -0.0805716229
## [1071] 0.9652212690 0.6744580243 -0.4945297161 -0.1035886412 0.6744580243
## [1076] 0.6821935466 1.4669131406 1.8161800514 1.8161800514 1.3705473838
## [1081] 0.9483637811 1.3784306740 -0.5130708359 0.3640339410 -0.5130708359
## [1086] -0.9418345631 0.5178170446 -0.1192679699 2.7752052933 2.7752052933
## [1091] 1.5977745444 -0.1350369366 -1.8640150455 0.6250735791 -1.1667348255
## [1096] 1.3833459029 -1.1667348255 0.5048807336 0.9676095272 0.5048807336
## [1101] 0.4238493295 -1.2578790406 -2.3571250047 -1.2578790406 -0.7561956706
## [1106] -2.0606272685 -0.1361446284 0.6526547323 -0.6013034462 2.2705329537
## [1111] -0.8178192052 -2.7170083534 -0.2578831168 1.2823604321 -2.4577856304
## [1116] -1.6558545214 -1.6558545214 -1.6558545214 -2.0180357789 -3.5235965067
## [1121] -0.8207138803 -2.7099348979 -3.2496795281 1.4103971729 -2.4949080705
## [1126] 0.1764756591 -2.4149244741 -2.9672855315 1.4636996652 1.6023043619
## [1131] -0.1752010446 -1.6709102997 -0.9540611058 -0.6145483318 0.0612159246
## [1136] -0.4246519111 1.4508338527 1.4508338527 0.5701224680 0.0274242293
## [1141] -0.3660102601 0.4560924793 -1.6395363665 -0.6665709969 -0.8175597534
## [1146] 0.2696369206 0.1810606054 1.3284913183 -0.3225616374 0.6934025275
## [1151] -0.1997322016 -2.3056415062 0.1859398140 0.6667852317 -2.4208052850
## [1156] 0.1859398140 0.4864959579 -2.6024972836 0.2159213758 1.6583614445
## [1161] 2.0700022370 0.6260656600 0.5049000296 -0.1945416101 -0.1945416101
## [1166] 3.4251623670 2.1034272622 0.1796100458 -0.9331316032 -0.6929864871
## [1171] 0.7444073910 -1.8229420646 0.8719045769 -0.3254967501 -0.3254967501
## [1176] -2.5524390270 -2.5873923623 -2.4263034856 -3.8985619000 0.4095080107
## [1181] 0.4095080107 0.3546933433 1.0841936754 -1.3667007237 -1.1283392301
## [1186] -1.3926637112 -2.0053672700 -1.3926637112 -1.1283392301 -1.3915018576
## [1191] 0.9902707793 -2.5117574729 -0.9039994711 -2.5398612722 -0.8832304488
## [1196] -1.3826877721 -0.0549877257 -0.6799679824 0.2028474917 -0.0549877257
## [1201] -0.6799679824 -0.4825220856 -0.1820473112 1.2404204924 -0.1107334743
## [1206] -0.1107334743 -0.1107334743 1.4603896002 -0.1107334743 -0.8634575564
## [1211] -1.8034550429 -0.9477134204 -1.8034550429 1.3328778439 1.4028652574
## [1216] 0.4104447487 0.0769227568 -0.1034668608 0.2222529396 0.4277499738
## [1221] 1.9718987973 1.9718987973 -0.3368289821 1.3531456794 3.3222142256
## [1226] 1.1491837228 -0.2700639859 0.5899207062 -4.0230494742 -0.2147655604
## [1231] -0.1829115511 -1.6868680167 -0.2147655604 0.9690916671 -3.1798981951
## [1236] 0.2943574083 -0.7816017377 -3.1798981951 -1.1457717714 -2.3752546738
## [1241] -0.7367160859 1.3503361782 -0.0573184075 0.0414066576 0.3946813448
## [1246] -1.1140315717 -1.1381319122 -1.1140315717 -0.8024693463 -1.8665899419
## [1251] -1.8665899419 -0.7593321936 -1.6893591896 -1.5813226087 -1.3597103372
## [1256] -2.9778562236 -0.5994819652 -0.2800348676 -1.8370623408 -1.8370623408
## [1261] 3.8135716975 -3.4918466765 1.2167260124 -1.1906258905 -0.5388651812
## [1266] -1.4563879281 -1.4563879281 1.7808295455 -0.7797767438 -3.5313343973
## [1271] -4.2480288975 -1.7625287765 -2.7907962427 -0.6407439226 -1.0982092152
## [1276] -0.3481681690 0.3197711270 -1.3580298383 -0.3481681690 0.6562404087
## [1281] -1.0022668087 -1.0022668087 -1.3337539817 0.3124952899 -1.3653578902
## [1286] 1.9613537555 -0.7731999967 -2.0020113895 1.0388964136 1.0388964136
## [1291] -1.7498644627 -0.6518071239 -2.7270150849 -1.5021698897 -0.6518071239
## [1296] -1.2850885782 -1.2850885782 -1.8843920755 -3.4463585007 -3.4338022796
## [1301] -3.8666899097 -2.3816811160 0.3590796173 -0.0721092193 -0.6268346437
## [1306] -0.7128922874 0.5788587051 -1.8832054282 0.5788587051 -1.1265872415
## [1311] -0.7128922874 -2.0438281189 -1.8464413894 -0.9565888478 -0.9757068117
## [1316] -0.4402844475 -5.2735227320 1.1925632588 -0.4402844475 4.4979236037
## [1321] 0.8913743409 -5.3859379293 0.1396590314 0.6808666792 -1.3229375613
## [1326] -1.3229375613 -1.3229375613 -1.3229375613 -1.4438833175 -0.5557270531
## [1331] -0.5557270531 -0.4696815623 -0.1434018196 0.1361393634 -1.5716959834
## [1336] -2.2901653658 -2.3407175525 -2.3407175525 -2.3407175525 -1.5102855614
## [1341] -1.5102855614 -1.5102855614 -1.4854102377 -1.5102855614 2.2569020918
## [1346] -0.2338915512 -2.7638842670 -1.7228297880 -1.7228297880 -1.9709342190
## [1351] -0.2422519065 -2.2594679588 -1.3967346976 -1.3967346976 -1.4991551921
## [1356] -1.3112408615 -1.3463186424 -1.3464121555 0.0117604039 1.9942162503
## [1361] 0.4823557454 -0.8807772879 1.9942162503 -0.7322174119 -2.1520702102
## [1366] 0.0562929054 -1.2271472992 0.7055913370 -0.4429682148 -2.4526908959
## [1371] 3.3962277813 1.0348964987 3.3962277813 -0.2716957971 -1.3947087885
## [1376] -0.5943041973 -0.9009568072 -2.5502177648 -0.8246809389 -1.3961813008
## [1381] -1.3961813008 -2.0732483693 -0.6590855259 -0.6590855259 -0.8648612664
## [1386] -0.3945438230 -1.0243362070 -1.0243362070 -0.1337890197 -0.9036883689
## [1391] -3.2493077164 -0.7894492038 -1.4837301863 -0.5610009514 -1.0270427428
## [1396] -0.3630314407 -0.3347959415 -0.6916598187 -1.5399441741 -1.8005059097
## [1401] -0.6474162718 -0.6474162718 -0.1264011949 0.6557880731 0.1707566861
## [1406] 0.0559714824 1.4233000025 -2.1582453922 0.2375777310 -2.1582453922
## [1411] -3.4547585137 -0.9528199722 1.4233000025 1.4580366017 2.5815682244
## [1416] -1.8702910556 2.5815682244 -0.4825113340 -1.0441771765 -0.9109256818
## [1421] -1.0441771765 -0.5688723260 -1.4969738227 -1.2460980581 0.8571556323
## [1426] 0.8571556323 -0.1308745432 -0.3164205011 -1.3217307733 0.4685956599
## [1431] -0.8152133251 -0.2449836197 -2.3231884793 -1.2948001488 4.6892061608
## [1436] 4.6892061608 2.3434472548 -1.8120340184 -2.0224343646 -1.4877941273
## [1441] -0.3571418001 -0.0599608098 -1.2369280491 -2.1534481272 -1.4913359468
## [1446] -0.0635026292 -1.2369280491 -1.3910351408 -1.3227198611 -0.3734247018
## [1451] -0.3571418001 0.8102833202 -2.1085423088 -0.3251825938 2.7308938355
## [1456] -2.9693557806 -2.5892628216 -0.3251825938 0.0098636151 -0.1555385574
## [1461] -1.1960924670 -2.5810432838 -1.7556814658 -1.8467769811 -1.5574628011
## [1466] -1.5574628011 -0.1368112793 -2.5671862185 -0.1368112793 -1.6037199010
## [1471] 0.5797231217 -2.5811277420 0.6147285146 -2.4556203915 4.5594461590
## [1476] -3.0360079427 4.5594461590 -3.0395497622 -1.1545766694 1.3144229861
## [1481] -2.9732528888 1.3144229861 0.2738955204 -0.1859145445 -2.4740905573
## [1486] -1.2880899422 -1.4277231867 -2.2994366213 -3.4877456561 -2.0016517136
## [1491] -0.9671066548 -3.4877456561 -3.2643434005 -0.1850186303 -1.5611512894
## [1496] -1.2422290457 -0.1850186303 -2.3204431296 -2.5959923564 -2.3204431296
## [1501] -1.4976477121 -0.0470169618 -0.8003947022 -1.0670181044 0.2064206766
## [1506] -2.2967191374 -2.2910625930 0.2064206766 0.8244068759 -0.0051054204
## [1511] -0.9553237634 -1.4713075764 -2.4097972658 -1.9227261606 -0.9023094109
## [1516] -0.9040803207 -0.4806482284 -1.5549756753 0.2330035473 -1.5594226597
## [1521] -1.5549756753 -1.1810615183 -0.4806482284 -1.0217676905 -1.1581174192
## [1526] -1.2733634284 -1.1064426087 -1.3206323043 0.8517232192 -1.7641091772
## [1531] -1.7790964684 -2.1578830176 -0.7055733266 -0.0460816996 -2.0419684423
## [1536] -1.1695315847 -2.3071765187 -2.5027326470 -2.4502594340 -0.3554878757
## [1541] -1.8471046339 -0.2133702694 -2.2802935375 1.3232082622 0.7447041003
## [1546] -1.1571560058 -1.5614873752 -3.1885468407 2.2298161376 -0.5004563897
## [1551] -1.4890130632 -1.4745282920 -2.0926431024 -1.3631894565 -2.5542057660
## [1556] -1.5402196223 -2.4275701206 -2.5542057660 1.0292631251 -0.3150794941
## [1561] -0.3150794941 -0.3150794941 -1.3306207562 -1.3306207562 -1.3306207562
## [1566] -1.8974422210 0.2464746288 -1.3306207562 -1.0739319797 -2.4019061267
## [1571] 0.2004534301 -1.7110684769 -0.7865023364 -2.3578747948 1.3793294014
## [1576] -0.5248894680 0.9133955317 -1.9040854922 -1.5199255901 -2.7479381549
## [1581] -0.7819785653 -2.7479381549 -3.2071294065 -0.8296831154 -0.2079615517
## [1586] -0.0399990188 -0.0714585911 -2.5227525478 -0.9721684607 -0.2367277732
## [1591] -1.7259228705 -3.4853588000 -1.4556735759 -1.2352310829 -2.1498275430
## [1596] -2.2138035086 -1.4556735759 -2.2698078356 -0.4268412180predict(df_pca)[,1]## [1] -1.6190233808 -0.7989199991 -0.7482450102 2.3569352844 -1.6190233808
## [6] -1.5832119779 -1.1011195532 -2.2480043771 -1.0865466057 0.6545847432
## [11] -1.1248523852 0.6545847432 -3.0259143987 1.5384164486 1.5647785548
## [16] 1.6207417506 1.1741982777 2.6943618301 -0.7222123697 3.3342696553
## [21] 0.6234495719 -0.2059201160 0.6961895123 0.0530890262 -0.8782805799
## [26] -1.1642698338 -0.2393189176 0.6961895123 -1.7346751593 -1.3219581716
## [31] -1.5592690050 -1.8050008848 -0.0895459518 -0.0867786299 -1.0837568763
## [36] -0.5614524459 -0.8278540247 0.4339239895 -2.9857396410 0.7641366874
## [41] 0.7641366874 0.3507712392 1.4009534162 0.3617225771 -2.1413294956
## [46] -4.2775856693 0.2331430018 1.2327080334 -0.9389723339 -0.6576468843
## [51] 0.5882230958 -1.5606289219 -1.5208686045 0.8461291444 -0.2549027582
## [56] -0.7112083086 2.3003794236 0.3128443129 -0.7529628698 -0.5177435570
## [61] 0.5326550505 0.7033615032 -0.6972955953 -1.5574365312 -1.7058880733
## [66] -1.7058880733 -1.0600479370 -1.9571393728 1.4023857653 -0.7577244093
## [71] -0.9999191681 -0.7695600669 -0.8562065492 -0.1462766933 2.0689381719
## [76] 0.9132489307 0.9132489307 -2.1682947506 -1.6932037651 0.8949146807
## [81] -1.3094380643 4.2819216263 0.1196024010 2.0986279913 -0.2790824419
## [86] -1.2983952432 3.3203917603 -0.5115931700 2.5076218099 -1.3568807729
## [91] -0.1747656004 3.3203917603 3.4094800686 -0.5115931700 -4.6345864975
## [96] -4.0927389504 -1.8938782706 -0.3217304729 -1.5393328235 -0.2192307179
## [101] -0.2904842848 -0.4650346765 -0.2192307179 -0.3440831820 -0.7784454102
## [106] -0.3440831820 4.4437241847 -0.7903975135 1.1210572328 0.7757945360
## [111] 0.3891875737 -0.0669049221 -0.0201665498 1.6116220721 0.3891875737
## [116] 1.1698071038 -0.1289330701 -0.8434294325 -0.2852066931 -1.3645292367
## [121] -1.1256407134 -0.2852066931 -1.5075644741 -1.2698331012 -0.9868665687
## [126] 0.6533937651 -2.8296888090 -2.8856553229 -0.6487334280 -1.4346891605
## [131] 0.7289523106 -3.1805724630 -3.1805724630 -1.9396816470 -1.3976085562
## [136] -0.0815424656 -0.0882906684 -0.3093735873 -0.6507117783 -0.6374338891
## [141] -0.0815424656 -0.0882906684 -3.8605516273 -1.3641258354 -3.8605516273
## [146] 0.9475601351 -2.8652698825 1.3496627748 -1.4143329865 0.0462106670
## [151] -0.0523283441 8.6506175515 -1.5478779955 -1.5478779955 0.2712096264
## [156] 0.2641282213 0.2712096264 0.2641282213 -1.8498615530 -1.6111528310
## [161] -1.2859639597 -0.1120192706 -1.0742579809 0.0226183909 -0.0200148627
## [166] 0.4788210839 -1.5048751507 -1.5753352298 -1.9719192034 2.8717600321
## [171] -1.7369389890 -0.0779569541 -0.0779569541 -1.7779866591 -0.6624433130
## [176] -1.0754554515 -0.6624433130 -0.6169172941 -1.9660188094 0.0007361586
## [181] 0.0007361586 2.6388040315 -1.5905338598 -1.3679174303 -1.3220828649
## [186] 1.3202913800 0.3512441109 -1.0268162818 0.3302101255 0.2941554682
## [191] 0.6849249373 -1.1089200492 -0.5337173226 -0.5312619075 -0.5312619075
## [196] -0.0562276348 -0.8386778425 3.1677209277 -3.6115490683 -3.2161574621
## [201] 1.4045752635 2.4110640638 -1.0590735011 -0.3823451122 -0.3370745742
## [206] 3.7214910153 3.7214910153 -0.3599005233 0.4468686366 2.1823798659
## [211] 0.8345613544 -0.5724415472 2.3488234226 -0.7419871933 -0.3748185502
## [216] 0.6051692411 -0.0858886685 -0.7007545958 -1.0078732101 -0.0096018066
## [221] 0.0959528102 -0.0436767558 -1.9279513770 0.1546931895 0.0784825203
## [226] -0.5985753362 3.8407190827 -0.3415062675 -0.5985753362 -1.1369815155
## [231] -3.3203963427 -0.9127565249 0.4593750278 -1.1369815155 -1.2030535048
## [236] -1.2458929692 -1.2458929692 -1.2898703502 -1.2458929692 -1.2030535048
## [241] 2.7052732283 2.9507230143 -0.4067664560 4.3834559142 4.3834559142
## [246] -1.1971770943 -1.4173947547 -0.3856903866 -1.2329684667 -1.1971770943
## [251] 2.0070741656 -1.4713199674 2.2019356945 -0.0855690526 -1.4713199674
## [256] -0.0533098400 1.3084545104 -1.7824423313 5.1529261306 1.8926316087
## [261] -0.0308251162 -1.8610808098 -1.1787535779 -0.0206363712 3.1265999169
## [266] 2.2239393306 -1.0016620752 0.6745583099 -1.1449991099 3.3436547984
## [271] -0.7246593448 3.3436547984 2.7705825728 -0.2353655344 0.4274125078
## [276] -0.7246593448 -1.1449991099 3.3436547984 2.0178157279 1.2353152770
## [281] 2.9573317128 3.4026121669 -0.6281781983 1.2353152770 0.8561730111
## [286] 0.8561730111 3.0286257824 -0.4958104021 0.0532249793 3.5725192019
## [291] 0.0532249793 3.1371058691 1.2856424772 -0.5789261714 4.3735065308
## [296] 2.6545793026 1.0258089683 -1.4935260117 -1.2479747539 -1.6813440640
## [301] -1.1261189476 1.9380230911 -0.4495046383 -0.8925519301 1.1161123396
## [306] 2.1596261591 -0.4909436553 2.5740362637 2.6158522254 -0.2271865925
## [311] 2.1596261591 -0.3754289614 0.7790545576 0.3709803383 -0.3182426983
## [316] -0.5324568811 0.9857340146 0.0682013843 0.8065195761 0.0682013843
## [321] 0.8065195761 1.0913378105 -0.5786493035 1.5382273586 2.7692650017
## [326] 2.7692650017 3.0873061094 2.0063423101 3.8459792045 1.5748601207
## [331] 2.1147976115 2.1147976115 0.1785995334 -0.3865884068 -1.1525342309
## [336] 2.6709356731 -0.1732675216 0.1843473050 3.6999638933 4.3587223617
## [341] 3.8788860031 1.8071734923 1.9883293821 1.9883293821 2.9957675795
## [346] -1.7610389940 -2.4211980941 4.0617329780 1.6795446529 0.0815424640
## [351] 1.8206498952 0.0657245690 -0.5213494466 4.3839964082 -1.3511585954
## [356] -2.7049351652 2.7225920015 2.1054762294 3.6377038912 3.7402299853
## [361] 0.2954347898 0.8135790155 2.9760271811 3.6706958376 4.3892799173
## [366] 1.9808037810 4.3892799173 2.5916278305 2.8673289530 1.9119913874
## [371] -1.8490125711 0.7865851323 1.3362106119 0.3247468020 4.7876029568
## [376] 3.5932507026 3.0130182802 1.9119913874 3.2331876694 0.6693528537
## [381] 1.3554499227 4.5920898329 1.3554499227 1.3554499227 0.0047823430
## [386] -0.7391286994 -0.0154886172 -0.5647850001 0.0275182472 1.4388821018
## [391] -3.6346031116 4.5920898329 1.5943891646 1.1997505915 3.2669997767
## [396] 3.1753769295 -0.7935544314 2.3157778903 2.3157778903 0.0193242204
## [401] -0.7935544314 -0.6203987865 2.9954685936 2.2776909622 -0.6129961912
## [406] 0.6127886296 1.3366507824 3.1079036467 2.4571333684 2.8640470336
## [411] 0.6983753098 0.7179048121 -0.6883471565 2.3208606124 0.8290338411
## [416] 1.9658957174 1.7342678135 -1.7268536455 2.8272604901 -2.2384733786
## [421] 0.5104560598 -2.9379453288 -1.9078394555 2.3092629911 -1.9078394555
## [426] -2.9379453288 -2.6698811458 0.2005993874 0.6336576249 2.8248308132
## [431] 2.3092629911 0.2126650030 2.5647785520 3.2132819200 2.8499349677
## [436] 3.2132819200 -0.6253324578 2.6750164540 2.8499349677 -0.5519972372
## [441] 4.4104777611 3.0487796426 5.5353026325 1.0486353799 -3.1366432904
## [446] 0.1004276569 3.3902207995 0.8056683330 0.1608407480 3.2824688943
## [451] 3.2824688943 3.9212462064 -1.5119182559 2.5537422970 -0.8503685741
## [456] 2.3663151025 0.5148554636 0.4877010264 2.5537422970 2.7506550682
## [461] 0.8213194793 0.2756233733 2.2887459152 1.5422026457 3.3111852800
## [466] 1.9520019204 1.5025887554 -0.0783413905 3.2260222552 -1.2199010844
## [471] 3.7806147396 0.9663272335 3.2880736770 1.5885087723 2.4564707668
## [476] 0.9269953314 1.4100404940 2.4585284773 0.9269953314 0.4882581689
## [481] 4.4331012560 1.9260510625 3.7219672565 3.7566418720 2.4835011368
## [486] 0.7730634181 0.7730634181 0.7754386549 2.8427578376 1.1325865363
## [491] 0.2546396254 0.2742794474 0.3838318675 0.2609030033 -1.0259683220
## [496] 1.9453179863 -0.1323202712 0.0746504001 1.9453179863 0.2609030033
## [501] -0.1323202712 3.0740056348 3.0740056348 2.4001570896 2.2312431689
## [506] 1.8009790711 2.1319952494 2.5085956265 1.3462990796 3.9267610227
## [511] 3.0233474835 1.3462990796 2.8208511914 2.2003808022 2.2003808022
## [516] 2.6435464998 2.8782225235 2.6338559267 1.9552765317 0.3565170016
## [521] 1.7066772449 0.3170780339 1.2406867883 1.3771074539 1.3844493799
## [526] 1.3410589741 0.3565170016 0.0726602276 1.2351886623 0.9444469397
## [531] 1.0135709739 2.9374344615 2.9374344615 0.4731931599 1.2182102551
## [536] 1.0135709739 0.9444469397 -0.5203604485 3.4414459596 3.0257055388
## [541] 0.8966116493 2.0027718933 0.3167933675 1.8242062565 5.5003604339
## [546] 1.8234269211 -0.2214901369 2.1861450699 3.0835287410 1.3531187498
## [551] -1.4168217862 1.1676454978 1.3578562148 -4.0639695509 5.0903446976
## [556] 5.0903446976 2.7259424294 5.1184484969 2.7259424294 3.0619207947
## [561] 2.4566123568 1.2598392395 1.7436745235 0.6937115471 3.0619207947
## [566] 2.4566123568 0.9750974328 0.9750974328 2.3670218236 -1.9079911544
## [571] 2.6843706293 -1.9079911544 2.0098230436 1.4051419287 2.4094212911
## [576] 3.0386193461 0.4492157693 1.1698659635 1.1485427391 1.8136205644
## [581] 2.5053310512 2.5053310512 2.3099668814 2.9384057937 2.6409076451
## [586] -0.3281823776 3.0698464544 -0.4137554716 -3.5323125244 2.2877256712
## [591] 1.0588377991 -0.6458707802 1.0588377991 2.2448292655 -0.7072350554
## [596] 1.6987890950 3.0816260835 2.4441081104 -0.1876797616 3.2686798844
## [601] -0.7750688861 3.3677954563 -1.1296098743 3.3677954563 0.5167240728
## [606] -0.0421897513 1.1372793216 0.7136544372 2.1477209667 -1.9138056976
## [611] 1.1725698019 3.9229649729 -1.2215029021 0.3909134134 2.3414616671
## [616] 0.7541133639 0.7541133639 3.2315705622 3.0988015816 2.7495032393
## [621] 0.3238192283 0.2413929414 0.7892964765 -0.2895111666 -0.5074863403
## [626] -0.5074863403 0.3023997610 0.3023997610 0.4546272277 -0.1502794070
## [631] 0.4546272277 2.3302690054 -0.5638691680 0.4840736254 -0.1916634547
## [636] -1.4545428918 0.2829088144 0.1791229257 -0.9912326773 1.9299475446
## [641] 1.0728456949 0.6986170436 1.0728456949 0.6986170436 1.0728456949
## [646] 0.0516123430 -0.9522409339 -1.3571472887 -0.0148188811 0.0730832368
## [651] 2.5930748917 0.5235881412 4.7092275291 1.7120439632 1.2847153310
## [656] 1.4348163962 2.5930748917 3.0161878260 -0.5469547541 -1.5265409477
## [661] -0.5469547541 0.2240558964 -0.9086472195 2.1680971829 3.3047694277
## [666] 0.5128181265 1.4488820420 3.0618655706 0.6594202008 3.0618655706
## [671] -0.4756540111 -0.3553170343 0.1911368607 -0.3553170343 2.2961796834
## [676] 1.0806917507 2.2961796834 -0.9156804674 -1.1359334190 1.9501037512
## [681] 3.5076923844 -0.1468564617 0.2196113221 -0.9502950855 0.1618231077
## [686] -0.9502950855 -1.1278954647 0.7754448806 -1.7295760640 1.7667765702
## [691] -2.6796319383 -0.2411098372 4.0863559656 0.7945698336 0.7795840176
## [696] -4.7570170106 -1.5027212153 -1.5027212153 0.8850818655 2.6020749372
## [701] 1.2548205104 -1.5027212153 -1.5210472578 0.6280637866 -0.5386257098
## [706] -0.5060846684 -1.9090352061 -0.8258935560 -1.0439575229 1.4297631105
## [711] 1.3480066416 0.8701260759 -0.5155006970 0.1142581515 0.3555203142
## [716] -0.6215288318 0.1142581515 -0.2946467984 -0.4353694587 -1.3497063692
## [721] -0.4353694587 1.0417457870 -0.2622641931 2.2930946569 -2.3695525657
## [726] -0.1248637338 -0.5077890181 -1.5866951178 -1.5866951178 -3.2959842750
## [731] 3.3703914361 -0.6431042654 -1.7307727314 -0.4942352456 -0.4854639045
## [736] -1.1760580802 -1.1760580802 -0.4484857621 1.1452016678 -0.3011096966
## [741] -0.6914087775 0.6551926626 -1.6462958348 2.9949658078 2.7799871931
## [746] -0.8001888147 0.5931393943 0.7604462726 -0.8479286141 -0.8001888147
## [751] -0.2675412924 -0.2675412924 -0.4823718117 -0.2675412924 3.5828218360
## [756] -2.5882975604 -3.5691862362 -1.2300729390 -1.2300729390 0.7072286790
## [761] 0.5070425191 0.1443651950 -0.9823951857 0.1443651950 0.0936464036
## [766] 0.1080593870 0.4711701416 0.1243047088 -1.7064929338 -1.4126382690
## [771] -1.7064929338 0.3363144163 0.5643953017 1.0051199931 0.9771608383
## [776] -2.2290091065 -0.6615625670 -0.5629150936 0.2880646630 -1.3426486215
## [781] -0.6217883321 -1.4268600629 -0.7527717281 -1.4268600629 -1.6451886176
## [786] 1.4574000102 1.4574000102 1.0388595499 1.0388595499 0.6379601043
## [791] 0.4544341965 0.2440909587 -1.3965051923 -1.4997217522 1.0696326891
## [796] 2.4479889198 1.3981878268 0.6149311533 0.8463407705 0.8463407705
## [801] -0.7433761582 -0.2952003988 -3.3005506786 -0.1836637810 0.0231833536
## [806] -0.4695237098 -0.2087256144 -0.4695237098 0.3007698951 0.0764251040
## [811] -1.1890236274 3.2756178513 1.5053947146 -1.2836164505 2.9763061398
## [816] 1.5053947146 0.7583820815 0.9937439682 -1.5672663308 0.5372440416
## [821] -1.4655912406 -4.1758506384 -1.6118208200 -1.6118208200 -0.2592232220
## [826] -1.1079324675 -0.5875044985 -1.1079324675 -1.9100280725 -2.6245202017
## [831] -1.5349910966 -2.6245202017 1.6253595491 1.9932304384 -0.3583699613
## [836] -1.3870102749 -2.1434101500 -2.1434101500 1.1958045738 -1.8895107902
## [841] 1.7265331598 -1.6786061161 1.5435149849 -0.3123181549 1.4467584987
## [846] -1.6376925175 -1.6376925175 -1.0578238539 -1.6376925175 -1.6135692582
## [851] 0.8383283727 0.8383283727 1.3424325811 0.1138528986 0.1138528986
## [856] -2.0694525691 0.1138528986 0.0781816742 1.9813340287 -1.3404938162
## [861] -1.4262102968 -2.5204988910 -0.2362628364 -1.4856381662 -1.4262102968
## [866] -1.4525606224 -1.2267239901 -1.1761199099 -1.3404938162 -1.6876496610
## [871] -2.4403725399 -2.2862066951 -0.9810409090 0.4863840013 1.6310899129
## [876] 0.7840480431 -1.7760809302 -2.4403725399 0.2933908880 -1.5251891591
## [881] -0.0843722099 -2.1788399203 0.8638678490 -1.5251891591 0.2933908880
## [886] -0.2467583576 -0.2762469851 1.0736712984 -2.1907976053 2.5781750541
## [891] -0.9635339420 -1.6319396954 0.9900930101 -1.6319396954 -1.5874385541
## [896] -1.8579646941 -0.0358885314 -1.8579646941 -0.0358885314 -1.5030461838
## [901] -0.0445460132 -1.4960116480 -1.4960116480 -1.3139119145 -1.3139119145
## [906] 0.5730783709 -0.5902039533 -2.8613085572 -1.4755169741 -0.9405889574
## [911] 0.9249772244 2.5187721457 0.6159549852 1.0191453893 -0.9405889574
## [916] 0.2477249174 -2.6185074429 0.5453972996 0.2524504255 -1.0499103951
## [921] 1.0335398033 0.2524504255 -1.0499103951 0.5453972996 0.1363671106
## [926] 0.0653092792 0.7298473824 0.1439260476 0.1363671106 0.1331766935
## [931] -2.1339230114 -1.3733506754 -0.4469897279 -1.3733506754 -2.1339230114
## [936] -0.0006385448 -0.0006385448 2.1181114655 -0.8709115498 -2.5480099542
## [941] 0.5648128008 1.0679246591 1.5899546566 1.2334433723 0.5046874206
## [946] 1.2571827796 2.0792416335 0.1329178189 0.4530389182 0.4530389182
## [951] 0.4530389182 0.1329178189 0.0651340999 0.8107129267 -0.7745710086
## [956] 0.8437243245 0.7114707118 0.8447671694 -1.6683654481 -0.7810361720
## [961] 0.2169593784 -0.8188084292 -1.1410638547 0.8854650000 0.2169593784
## [966] 0.3022742374 0.8801810210 0.2374852901 0.1230599105 -1.3964723164
## [971] 1.5866049448 1.5866049448 1.3684382856 -0.2214779996 0.7830179587
## [976] -0.6043311386 -0.6043311386 0.7243532532 -0.8696541856 2.0654286326
## [981] 0.4130091449 -0.0026164093 -1.0392038117 0.4130091449 2.0654286326
## [986] -1.9649983508 0.4074557373 -0.6759148479 -0.2863638456 0.7950246409
## [991] -0.2863638456 -0.6889745007 -1.4422301438 -0.6889745007 1.9162762057
## [996] -1.0788223830 -3.6508563635 -3.6508563635 -0.1852979109 -3.6051215740
## [1001] -0.9397047159 1.0092364511 0.4493997617 -1.6153190699 -0.1567545890
## [1006] -1.6153190699 0.4493997617 0.4612273395 0.7755577952 1.1878309015
## [1011] -0.1064233368 0.5021854794 -1.1993248862 -1.5729317663 -0.7143899278
## [1016] 1.8569440847 0.1662851728 -0.5158032245 -0.5158032245 -1.5305735079
## [1021] 2.0564703173 2.0564703173 -1.4018563833 0.0474004970 -1.3434856892
## [1026] -0.7400140317 -1.1786805220 -3.1673972353 -0.8291878973 -1.3434856892
## [1031] -2.0693199544 -1.6079546499 -0.7452891991 -0.9721179817 -0.1323453435
## [1036] 1.2536645651 -0.5467211896 -1.4741802592 0.7246510092 0.1562830487
## [1041] -2.0820949410 -1.0940532504 0.1562830487 2.0225003397 -1.4851746237
## [1046] -1.9222880010 -1.5942850527 -1.2060379166 0.7409166582 0.7437478254
## [1051] -1.2060379166 4.2162276498 -3.4968403938 -0.2841906742 0.6040498059
## [1056] 0.6040498059 0.8997341230 0.7972954214 1.5575547700 0.8997341230
## [1061] 2.7951299384 0.4593969268 0.1638407106 1.5322471535 -1.6056656004
## [1066] -0.6127640740 -2.0731462052 2.3549073368 2.3549073368 -0.0805716229
## [1071] 0.9652212690 0.6744580243 -0.4945297161 -0.1035886412 0.6744580243
## [1076] 0.6821935466 1.4669131406 1.8161800514 1.8161800514 1.3705473838
## [1081] 0.9483637811 1.3784306740 -0.5130708359 0.3640339410 -0.5130708359
## [1086] -0.9418345631 0.5178170446 -0.1192679699 2.7752052933 2.7752052933
## [1091] 1.5977745444 -0.1350369366 -1.8640150455 0.6250735791 -1.1667348255
## [1096] 1.3833459029 -1.1667348255 0.5048807336 0.9676095272 0.5048807336
## [1101] 0.4238493295 -1.2578790406 -2.3571250047 -1.2578790406 -0.7561956706
## [1106] -2.0606272685 -0.1361446284 0.6526547323 -0.6013034462 2.2705329537
## [1111] -0.8178192052 -2.7170083534 -0.2578831168 1.2823604321 -2.4577856304
## [1116] -1.6558545214 -1.6558545214 -1.6558545214 -2.0180357789 -3.5235965067
## [1121] -0.8207138803 -2.7099348979 -3.2496795281 1.4103971729 -2.4949080705
## [1126] 0.1764756591 -2.4149244741 -2.9672855315 1.4636996652 1.6023043619
## [1131] -0.1752010446 -1.6709102997 -0.9540611058 -0.6145483318 0.0612159246
## [1136] -0.4246519111 1.4508338527 1.4508338527 0.5701224680 0.0274242293
## [1141] -0.3660102601 0.4560924793 -1.6395363665 -0.6665709969 -0.8175597534
## [1146] 0.2696369206 0.1810606054 1.3284913183 -0.3225616374 0.6934025275
## [1151] -0.1997322016 -2.3056415062 0.1859398140 0.6667852317 -2.4208052850
## [1156] 0.1859398140 0.4864959579 -2.6024972836 0.2159213758 1.6583614445
## [1161] 2.0700022370 0.6260656600 0.5049000296 -0.1945416101 -0.1945416101
## [1166] 3.4251623670 2.1034272622 0.1796100458 -0.9331316032 -0.6929864871
## [1171] 0.7444073910 -1.8229420646 0.8719045769 -0.3254967501 -0.3254967501
## [1176] -2.5524390270 -2.5873923623 -2.4263034856 -3.8985619000 0.4095080107
## [1181] 0.4095080107 0.3546933433 1.0841936754 -1.3667007237 -1.1283392301
## [1186] -1.3926637112 -2.0053672700 -1.3926637112 -1.1283392301 -1.3915018576
## [1191] 0.9902707793 -2.5117574729 -0.9039994711 -2.5398612722 -0.8832304488
## [1196] -1.3826877721 -0.0549877257 -0.6799679824 0.2028474917 -0.0549877257
## [1201] -0.6799679824 -0.4825220856 -0.1820473112 1.2404204924 -0.1107334743
## [1206] -0.1107334743 -0.1107334743 1.4603896002 -0.1107334743 -0.8634575564
## [1211] -1.8034550429 -0.9477134204 -1.8034550429 1.3328778439 1.4028652574
## [1216] 0.4104447487 0.0769227568 -0.1034668608 0.2222529396 0.4277499738
## [1221] 1.9718987973 1.9718987973 -0.3368289821 1.3531456794 3.3222142256
## [1226] 1.1491837228 -0.2700639859 0.5899207062 -4.0230494742 -0.2147655604
## [1231] -0.1829115511 -1.6868680167 -0.2147655604 0.9690916671 -3.1798981951
## [1236] 0.2943574083 -0.7816017377 -3.1798981951 -1.1457717714 -2.3752546738
## [1241] -0.7367160859 1.3503361782 -0.0573184075 0.0414066576 0.3946813448
## [1246] -1.1140315717 -1.1381319122 -1.1140315717 -0.8024693463 -1.8665899419
## [1251] -1.8665899419 -0.7593321936 -1.6893591896 -1.5813226087 -1.3597103372
## [1256] -2.9778562236 -0.5994819652 -0.2800348676 -1.8370623408 -1.8370623408
## [1261] 3.8135716975 -3.4918466765 1.2167260124 -1.1906258905 -0.5388651812
## [1266] -1.4563879281 -1.4563879281 1.7808295455 -0.7797767438 -3.5313343973
## [1271] -4.2480288975 -1.7625287765 -2.7907962427 -0.6407439226 -1.0982092152
## [1276] -0.3481681690 0.3197711270 -1.3580298383 -0.3481681690 0.6562404087
## [1281] -1.0022668087 -1.0022668087 -1.3337539817 0.3124952899 -1.3653578902
## [1286] 1.9613537555 -0.7731999967 -2.0020113895 1.0388964136 1.0388964136
## [1291] -1.7498644627 -0.6518071239 -2.7270150849 -1.5021698897 -0.6518071239
## [1296] -1.2850885782 -1.2850885782 -1.8843920755 -3.4463585007 -3.4338022796
## [1301] -3.8666899097 -2.3816811160 0.3590796173 -0.0721092193 -0.6268346437
## [1306] -0.7128922874 0.5788587051 -1.8832054282 0.5788587051 -1.1265872415
## [1311] -0.7128922874 -2.0438281189 -1.8464413894 -0.9565888478 -0.9757068117
## [1316] -0.4402844475 -5.2735227320 1.1925632588 -0.4402844475 4.4979236037
## [1321] 0.8913743409 -5.3859379293 0.1396590314 0.6808666792 -1.3229375613
## [1326] -1.3229375613 -1.3229375613 -1.3229375613 -1.4438833175 -0.5557270531
## [1331] -0.5557270531 -0.4696815623 -0.1434018196 0.1361393634 -1.5716959834
## [1336] -2.2901653658 -2.3407175525 -2.3407175525 -2.3407175525 -1.5102855614
## [1341] -1.5102855614 -1.5102855614 -1.4854102377 -1.5102855614 2.2569020918
## [1346] -0.2338915512 -2.7638842670 -1.7228297880 -1.7228297880 -1.9709342190
## [1351] -0.2422519065 -2.2594679588 -1.3967346976 -1.3967346976 -1.4991551921
## [1356] -1.3112408615 -1.3463186424 -1.3464121555 0.0117604039 1.9942162503
## [1361] 0.4823557454 -0.8807772879 1.9942162503 -0.7322174119 -2.1520702102
## [1366] 0.0562929054 -1.2271472992 0.7055913370 -0.4429682148 -2.4526908959
## [1371] 3.3962277813 1.0348964987 3.3962277813 -0.2716957971 -1.3947087885
## [1376] -0.5943041973 -0.9009568072 -2.5502177648 -0.8246809389 -1.3961813008
## [1381] -1.3961813008 -2.0732483693 -0.6590855259 -0.6590855259 -0.8648612664
## [1386] -0.3945438230 -1.0243362070 -1.0243362070 -0.1337890197 -0.9036883689
## [1391] -3.2493077164 -0.7894492038 -1.4837301863 -0.5610009514 -1.0270427428
## [1396] -0.3630314407 -0.3347959415 -0.6916598187 -1.5399441741 -1.8005059097
## [1401] -0.6474162718 -0.6474162718 -0.1264011949 0.6557880731 0.1707566861
## [1406] 0.0559714824 1.4233000025 -2.1582453922 0.2375777310 -2.1582453922
## [1411] -3.4547585137 -0.9528199722 1.4233000025 1.4580366017 2.5815682244
## [1416] -1.8702910556 2.5815682244 -0.4825113340 -1.0441771765 -0.9109256818
## [1421] -1.0441771765 -0.5688723260 -1.4969738227 -1.2460980581 0.8571556323
## [1426] 0.8571556323 -0.1308745432 -0.3164205011 -1.3217307733 0.4685956599
## [1431] -0.8152133251 -0.2449836197 -2.3231884793 -1.2948001488 4.6892061608
## [1436] 4.6892061608 2.3434472548 -1.8120340184 -2.0224343646 -1.4877941273
## [1441] -0.3571418001 -0.0599608098 -1.2369280491 -2.1534481272 -1.4913359468
## [1446] -0.0635026292 -1.2369280491 -1.3910351408 -1.3227198611 -0.3734247018
## [1451] -0.3571418001 0.8102833202 -2.1085423088 -0.3251825938 2.7308938355
## [1456] -2.9693557806 -2.5892628216 -0.3251825938 0.0098636151 -0.1555385574
## [1461] -1.1960924670 -2.5810432838 -1.7556814658 -1.8467769811 -1.5574628011
## [1466] -1.5574628011 -0.1368112793 -2.5671862185 -0.1368112793 -1.6037199010
## [1471] 0.5797231217 -2.5811277420 0.6147285146 -2.4556203915 4.5594461590
## [1476] -3.0360079427 4.5594461590 -3.0395497622 -1.1545766694 1.3144229861
## [1481] -2.9732528888 1.3144229861 0.2738955204 -0.1859145445 -2.4740905573
## [1486] -1.2880899422 -1.4277231867 -2.2994366213 -3.4877456561 -2.0016517136
## [1491] -0.9671066548 -3.4877456561 -3.2643434005 -0.1850186303 -1.5611512894
## [1496] -1.2422290457 -0.1850186303 -2.3204431296 -2.5959923564 -2.3204431296
## [1501] -1.4976477121 -0.0470169618 -0.8003947022 -1.0670181044 0.2064206766
## [1506] -2.2967191374 -2.2910625930 0.2064206766 0.8244068759 -0.0051054204
## [1511] -0.9553237634 -1.4713075764 -2.4097972658 -1.9227261606 -0.9023094109
## [1516] -0.9040803207 -0.4806482284 -1.5549756753 0.2330035473 -1.5594226597
## [1521] -1.5549756753 -1.1810615183 -0.4806482284 -1.0217676905 -1.1581174192
## [1526] -1.2733634284 -1.1064426087 -1.3206323043 0.8517232192 -1.7641091772
## [1531] -1.7790964684 -2.1578830176 -0.7055733266 -0.0460816996 -2.0419684423
## [1536] -1.1695315847 -2.3071765187 -2.5027326470 -2.4502594340 -0.3554878757
## [1541] -1.8471046339 -0.2133702694 -2.2802935375 1.3232082622 0.7447041003
## [1546] -1.1571560058 -1.5614873752 -3.1885468407 2.2298161376 -0.5004563897
## [1551] -1.4890130632 -1.4745282920 -2.0926431024 -1.3631894565 -2.5542057660
## [1556] -1.5402196223 -2.4275701206 -2.5542057660 1.0292631251 -0.3150794941
## [1561] -0.3150794941 -0.3150794941 -1.3306207562 -1.3306207562 -1.3306207562
## [1566] -1.8974422210 0.2464746288 -1.3306207562 -1.0739319797 -2.4019061267
## [1571] 0.2004534301 -1.7110684769 -0.7865023364 -2.3578747948 1.3793294014
## [1576] -0.5248894680 0.9133955317 -1.9040854922 -1.5199255901 -2.7479381549
## [1581] -0.7819785653 -2.7479381549 -3.2071294065 -0.8296831154 -0.2079615517
## [1586] -0.0399990188 -0.0714585911 -2.5227525478 -0.9721684607 -0.2367277732
## [1591] -1.7259228705 -3.4853588000 -1.4556735759 -1.2352310829 -2.1498275430
## [1596] -2.2138035086 -1.4556735759 -2.2698078356 -0.4268412180Univariate plot to understand :
[1] Based on barplot PC1 is good indicator for bad quality wine as most of the data points for bad quality wine are above max line as outliers
[2] Based on barplot PC2 is good indicator for good quality wine as most of the data points for good quality wine are below minimum line as outliers
[3] Based on barplot PC3 is good indicator for good quality wine as most of the data points for good quality wine are above max line as outliers
#The aboved two gives us the same thing. predict is a good function to know.
df$quality <- as.factor(df$quality)
out <- sapply(1:5, function(i){plot(df$quality,df_pca$x[,i],xlab=paste("PC",i,sep=""),ylab="quality")})
pairs(df_pca$x[,1:5], ylim = c(-6,4),xlim = c(-6,4),panel=function(x,y,...){text(x,y,df$quality)})
****Explain the variate representation each PCs**** Multivariate plot:
This used to see contribution of each variable in PC1 to understand variability in data
PC1: [1] fixed acidity, citric acidity and pH are highest contributors towards PC1
PC2: [2] Free sulfur dioxide and total sulfur dioxide are the biggest contributors.
Hence we can deduce that wine quality is good when PC2 is low which means Free sulfur dioxide content and total sulfur dioxide content also should be low
PC3: [3] Alcohol and volatile acidity are the biggest contributors to PC3
Hence we can deduce that wine quality is good when PC3 is high which means Alcohol content and volatile acid content also should be high
library(ggplot2)
# Extract the loadings from the rotation matrix
loadings <- abs(df_pca$rotation[, 1]) # Assuming you want to visualize the loadings for the first principal component
# Create a data frame for plotting
loadings_df <- data.frame(Attribute = colnames(new_df), Loading = loadings)
# Plot the contributions of each attribute to the first principal component
ggplot(loadings_df, aes(x = Attribute, y = Loading)) +
geom_bar(stat = "identity") +
labs(x = "Attribute",
y = "Loading (Absolute Value)",
title = "Contribution of Attributes to First Principal Component") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for better readability
# Extract the loadings from the rotation matrix for PC2
loadings_pc2 <- abs(df_pca$rotation[, 2])
loadings_df_pc2 <- data.frame(Attribute = colnames(new_df), Loading = loadings_pc2)
# Extract the loadings from the rotation matrix for PC3
loadings_pc3 <- abs(df_pca$rotation[, 3])
loadings_df_pc3 <- data.frame(Attribute = colnames(new_df), Loading = loadings_pc3)
# Plot the contributions of each attribute to PC2
ggplot(loadings_df_pc2, aes(x = Attribute, y = Loading)) +
geom_bar(stat = "identity") +
labs(x = "Attribute",
y = "Loading (Absolute Value)",
title = "Contribution of Attributes to Second Principal Component") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for better readability
# Plot the contributions of each attribute to PC3
ggplot(loadings_df_pc3, aes(x = Attribute, y = Loading)) +
geom_bar(stat = "identity") +
labs(x = "Attribute",
y = "Loading (Absolute Value)",
title = "Contribution of Attributes to Third Principal Component") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for better readability
# Better Ways to Visualize
library(factoextra)## Warning: package 'factoextra' was built under R version 4.3.2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)
library(ggfortify)## Warning: package 'ggfortify' was built under R version 4.3.2library(psych)## Warning: package 'psych' was built under R version 4.3.3##
## Attaching package: 'psych'## The following object is masked from 'package:car':
##
## logit## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(corrplot)## Warning: package 'corrplot' was built under R version 4.3.2## corrplot 0.92 loadedlibrary(devtools)## Warning: package 'devtools' was built under R version 4.3.2## Loading required package: usethis## Warning: package 'usethis' was built under R version 4.3.2Based on correlation plot below we can see that alcohol, citric acid, volatile acid and sulfur dioxide are skewed towards left side which is correlated towards either good or bad value of dependent variable as per extreme skewedness.
# Correlation
pairs.panels(df[,-1],
gap = 0,
bg = c("red", "blue")[df$quality],
pch=21)
PC2, PC3, PC4 and PC5 seem to be highly informative as they are either skewed towards left or right unlike PC1 which uniform and would not help us to predict dependent variable
pairs.panels(df_pca$x,
gap=0,
bg = c("red", "blue")[df$quality],
pch=21)
As per the below plot we can infer that PC1-PC5 is enough for analyzing data as they contain 85% of varainces which can be used for prediction and analysis
fviz_eig(df_pca, addlabels = TRUE)
As we have analyzed that PC2 and PC3 are good indicators of wine quality.
We can observe that alcohol and acid based variables are in positive curve of PC2 and PC3 indicating that good quality wine is majorly dependent on these attributes to be good.
library(factoextra)
# Plot variables' contributions to both PC2 and PC3
plot_pc2_pc3 <- fviz_pca_var(df_pca, axes = c(2, 3), col.var = "cos2",
gradient.cols = c("#FFCC00", "#CC9933", "#660033", "#330033"),
repel = TRUE) +
ggtitle("PC2 and PC3 - Variance Contributions")
# Display the plot
plot_pc2_pc3
repel = TRUE)
# Different PCA Method.
res.pca <- PCA(df[, 1:11], graph = FALSE)
print(res.pca)## **Results for the Principal Component Analysis (PCA)**
## The analysis was performed on 1599 individuals, described by 11 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. for the variables"
## 4 "$var$cor" "correlations variables - dimensions"
## 5 "$var$cos2" "cos2 for the variables"
## 6 "$var$contrib" "contributions of the variables"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "summary statistics"
## 12 "$call$centre" "mean of the variables"
## 13 "$call$ecart.type" "standard error of the variables"
## 14 "$call$row.w" "weights for the individuals"
## 15 "$call$col.w" "weights for the variables"Dim.1:
Free Sulfur Dioxide (26.375087) Fixed Acidity (23.9428401) Citric Acid (21.4954313) Dim.2:
Residual Sugar (7.402766) Volatile Acidity (7.558677) Free Sulfur Dioxide (1.221086) Dim.3:
Fixed Acidity (1.5203278) Volatile Acidity (20.2466277) Free Sulfur Dioxide (18.3863327) Dim.4:
Chlorides (44.3815454) Residual Sugar (13.8974295) Volatile Acidity (0.6234647) Dim.5:
Residual Sugar (53.6035258) Free Sulfur Dioxide (2.5329353) Volatile Acidity (4.7844791) Top 3 Attributes with Highest Negative Loadings:
Dim.1:
Volatile Acidity (-0.42001233) Chlorides (-0.37364637) Free Sulfur Dioxide (-0.06365298) Dim.2:
Fixed Acidity (-0.1533525) Citric Acid (-0.2106517) Chlorides (-0.2054617) Dim.3:
Volatile Acidity (-0.5602970) Fixed Acidity (-0.1535361) Citric Acid (0.2966672) Dim.4:
Fixed Acidity (-0.25291627) Citric Acid (-0.08747670) Volatile Acidity (0.08697170) Dim.5:
Fixed Acidity (-0.08091468) Citric Acid (-0.05736810) Volatile Acidity (0.21423615)
PC1: has high cosine similarity with fixed acidity, citric acid, density and pH but still it is evenly distributed hence not meaningful
PC2: has high cosine similarity with total and free sulfuric acid but as per previous analysis high PC2 means bad quality wine hence these attributes contribute towards bad quality wine
PC3: has high cosine similarity to alcohol and volatile acidity, this combination is correlated to good quality wine
library(corrplot)
# Compute the cos2 values from the PCA results
cos2_matrix <- res.pca$var$cos2
# Plot the correlation matrix using corrplot
corrplot(cos2_matrix, is.corr = FALSE)
Below plot confirms on our hypothesis that alcohol and sulfur components contribute towards PC3 and PC2
# Total cos2 of variables on Dim.3 and Dim.2
#A high cos2 indicates a good representation of the variable on the principal component.
#In this case the variable is positioned close to the circumference of the correlation circle.
#A low cos2 indicates that the variable is not perfectly represented by the PCs.
#In this case the variable is close to the center of the circle.
fviz_cos2(res.pca, choice = "var", axes = 2:3)
Below plot also confirms
as alcohol is positive with PC3 but negative with PC2 indicating good quality wine has high alcohol content
fviz_pca_var(res.pca, col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE,
axes = c(3, 2) # Specify PC3 and PC4
)
Hypothesis: High alcohol content, density and volatile acidity are contributors towards good quality wine as per observation as these categories are above mean line
# Contributions of variables to PC1
fviz_contrib(res.pca, choice = "var", axes = 3, top = 10)
As per descriptive analysis I can infer these values
For PC1:
Fixed acidity (correlation: 0.86140602) pH (correlation: -0.77198543) Density (correlation: 0.69599339) For PC2:
Total sulfur dioxide (correlation: 0.79031776) Free sulfur dioxide (correlation: 0.71271338) Volatile acidity (correlation: -0.42001233) For PC3:
Residual sugar (correlation: 0.71708742) Alcohol (correlation: -0.53593092) Chlorides (correlation: 0.37364637)
# Description of PC
res.desc <- dimdesc(res.pca, axes = c(1,2,3,4,5), proba = 0.05)
# Description of dimension 1
res.desc$Dim.1##
## Link between the variable and the continuous variables (R-square)
## =================================================================================
## correlation
## fixed acidity 0.86140602
## citric acid 0.81619353
## density 0.69599339
## sulphates 0.42764729
## chlorides 0.37364637
## residual sugar 0.25721219
## free sulfur dioxide -0.06365298
## alcohol -0.19933772
## volatile acidity -0.42001233
## pH -0.77198543
## p.value
## fixed acidity 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## citric acid 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## density 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000005427610470930229363481500648447308776667341589927673339843750000000000000000000000000000000
## sulphates 0.0000000000000000000000000000000000000000000000000000000000000000000000042387543636631847066356670872266931837657466530799865722656250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## chlorides 0.0000000000000000000000000000000000000000000000000000037991878355906329460447284906976506135833915323019027709960937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## residual sugar 0.0000000000000000000000001410737216867424874237879617311364199849776923656463623046875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## free sulfur dioxide 0.0108991717187046227577340573589026462286710739135742187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## alcohol 0.0000000000000008578273092887684008606141716768433980178087949752807617187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## volatile acidity 0.0000000000000000000000000000000000000000000000000000000000000000000023487249577389349049216793474670339492149651050567626953125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## pH 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001383551res.desc$Dim.2##
## Link between the variable and the continuous variables (R-square)
## =================================================================================
## correlation
## total sulfur dioxide 0.79031776
## free sulfur dioxide 0.71271338
## volatile acidity 0.38154068
## residual sugar 0.37758520
## density 0.32414940
## chlorides 0.20546173
## sulphates -0.05211625
## fixed acidity -0.15335255
## citric acid -0.21065171
## alcohol -0.53593092
## p.value
## total sulfur dioxide 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## free sulfur dioxide 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000003229371
## volatile acidity 0.00000000000000000000000000000000000000000000000000000001443574981081195070112613287882652457483345642685890197753906250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## residual sugar 0.00000000000000000000000000000000000000000000000000000024015591262574092316917084843908014590851962566375732421875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## density 0.00000000000000000000000000000000000000019483273416958411260874184378621976065915077924728393554687500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## chlorides 0.00000000000000010573703561019982035676031961735077402408933266997337341308593750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## sulphates 0.03717972766320236377435293206872302107512950897216796875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## fixed acidity 0.00000000070982889369286640479303485129491946281632408499717712402343750000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## citric acid 0.00000000000000001701654788304753126342364066836410074756713584065437316894531250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## alcohol 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001427117326466920995851547782073964754090411588549613952636718750000000000000000000000000000000000000000000000000000000000000000000000000res.desc$Dim.3##
## Link between the variable and the continuous variables (R-square)
## =================================================================================
## correlation
## alcohol 0.58733137
## free sulfur dioxide 0.53393641
## total sulfur dioxide 0.40147319
## sulphates 0.34839201
## citric acid 0.29666720
## residual sugar 0.12611890
## pH 0.07184521
## chlorides -0.11532350
## fixed acidity -0.15353613
## density -0.42196539
## volatile acidity -0.56029703
## p.value
## alcohol 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000006632283
## free sulfur dioxide 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000015584789116505351399058487693594088341
## total sulfur dioxide 0.00000000000000000000000000000000000000000000000000000000000000555112673958350554340168692135648598195984959602355957031250000000000000000000000000000000000
## sulphates 0.00000000000000000000000000000000000000000000007630616372127125938799874216300622720154933631420135498046875000000000000000000000000000000000000000000000000
## citric acid 0.00000000000000000000000000000000075285638670497517159312106382884621780249290168285369873046875000000000000000000000000000000000000000000000000000000000000
## residual sugar 0.00000042046909353701888640078565551050360227236524224281311035156250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## pH 0.00404823459784410446399283145524350402411073446273803710937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## chlorides 0.00000377637479046437500498217820776858388853725045919418334960937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## fixed acidity 0.00000000067709585102803639101130828148455975679098628461360931396484375000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## density 0.00000000000000000000000000000000000000000000000000000000000000000000047394567209749809172554535496857397447456605732440948486328125000000000000000000000000
## volatile acidity 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000077625050230366088623024res.desc$Dim.4##
## Link between the variable and the continuous variables (R-square)
## =================================================================================
## correlation
## chlorides 0.7337924
## sulphates 0.6067685
## volatile acidity 0.0869717
## citric acid -0.0874767
## alcohol -0.1345786
## density -0.1922060
## fixed acidity -0.2529163
## residual sugar -0.4106192
## p.value
## chlorides 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000002051535
## sulphates 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000019821410449065145551253430689087053906405344605445861816406250000000000000000000000000000000000000000000000000000000
## volatile acidity 0.000498316172625414346986083558022073702886700630187988281250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## citric acid 0.000461860319424522069910871158171516981383319944143295288085937500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## alcohol 0.000000065959427342164387688197568770931411563651636242866516113281250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## density 0.000000000000009030428060202416035151529172253503929823637008666992187500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## fixed acidity 0.000000000000000000000000932139643765448967522441181543513266660738736391067504882812500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## residual sugar 0.000000000000000000000000000000000000000000000000000000000000000004470531003062452689474082223597406482440419495105743408203125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000res.desc$Dim.5##
## Link between the variable and the continuous variables (R-square)
## =================================================================================
## correlation
## residual sugar 0.71708742
## alcohol 0.34346950
## pH 0.26202790
## chlorides 0.24143150
## sulphates 0.22131521
## volatile acidity 0.21423615
## density 0.15384636
## citric acid -0.05736810
## fixed acidity -0.08091468
## free sulfur dioxide -0.15587895
## total sulfur dioxide -0.21788948
## p.value
## residual sugar 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001176124
## alcohol 0.000000000000000000000000000000000000000000001699318834917645677827849337582222233322681859135627746582031250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## pH 0.000000000000000000000000016282377638092397594601207222453354006574954837560653686523437500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## chlorides 0.000000000000000000000121988332223686033633277409071737906742782797664403915405273437500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## sulphates 0.000000000000000000342269317522519176235154536591664964362280443310737609863281250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## volatile acidity 0.000000000000000004684064160466513348449885123514491169771645218133926391601562500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## density 0.000000000625094441651685903994783988224526183330453932285308837890625000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## citric acid 0.021784615656606783062887444657462765462696552276611328125000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## fixed acidity 0.001202206317184519252308505521398274140665307641029357910156250000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## free sulfur dioxide 0.000000000368833135850950954125193614885347415111027657985687255859375000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
## total sulfur dioxide 0.000000000000000001227967200531068367279352582244200675631873309612274169921875000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000# Graph of Indiviuals
ind <- get_pca_ind(res.pca)
ind## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"
#To get access to the different components, use this:
# Coordinates of individuals
head(ind$coord)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## 1 -1.6195299 0.4509501 -1.7744541 0.04374031 0.06701448
## 2 -0.7991699 1.8565531 -0.9116902 0.54806597 -0.01839156
## 3 -0.7484791 0.8820389 -1.1713942 0.41102067 -0.04353101
## 4 2.3576726 -0.2699760 0.2434887 -0.92844998 -1.49914907
## 5 -1.6195299 0.4509501 -1.7744541 0.04374031 0.06701448
## 6 -1.5837073 0.5691949 -1.5382861 0.02374989 -0.11007597- Model Insights (10 points): PCA
As per below plot (area curve)
High value of of PC3 indicates good score of wine and drops quickly below elbow point at 0.25
fviz_cos2(res.pca, choice = "ind", axes = 3)
- Model Insights (10 points): PCA
Finally we can confirm on our thesis that PC3 is strong indicator of good quality wine as the circle highlighting good quality wine is more towards positive of PC3
fviz_pca_ind(res.pca,
geom.ind = "point", # show points only (not "text")
col.ind = df$quality, # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE, # Concentration ellipses
axes = c(2, 3), # PC2 and PC3
legend.title = "Groups"
)
In the below plot we can infer that bad quality wine is towrds negative coordinates of PC2 w.r.t to PC1 showing negative correlation with quality of wines
fviz_pca_ind(res.pca, geom.ind = "point", col.ind = df$quality,
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE, ellipse.type = "confidence",
legend.title = "Groups"
)
PC4 and PC5 are not good indicators of quality of wine as the data points are evenly distribute towards center and also has random outliers.
fviz_pca_ind(res.pca,
label = "none", # hide individual labels
habillage = df$quality, # color by groups
addEllipses = TRUE, # Concentration ellipses
axes = c(4, 5), # PC4 and PC5
palette = "jco"
)
- Model Insights (10 points): PCA
Alcohol is shows contribution towards good quality wine, along with citric acid which shows little contribution.
Hence we can say that these 2 parameters indicate good rating for wine
Whereas density and volatile acidity show negative correlation and indicate bad quality wine
fviz_pca_biplot(res.pca,
# Individuals
geom.ind = "point",
fill.ind = df$quality, col.ind = "black",
pointshape = 21, pointsize = 2,
palette = "jco",
addEllipses = TRUE,
# Variables
alpha.var = "contrib", col.var = "contrib",
gradient.cols = "RdYlBu",
axes = c(2, 3), # PC2 and PC3
legend.title = list(fill = "Quality", color = "Contrib")
)
# Load required libraries
library(cluster) # For cluster analysis## Warning: package 'cluster' was built under R version 4.3.3library(readr) # For reading data files
library(factoextra) # For visualizing multivariate analysis results
library(magrittr) # For using the pipe operator %>%##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractlibrary(NbClust) # For determining the optimal number of clusters#for loading csv data
library(readr)
# Read the CSV file
df <- read_csv("C:/Rutgers/Subjects/Spring Sem/Multivariate Analysis/Data/wine_new.csv")## Rows: 1143 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): quality
## dbl (11): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#dataframe
df## # A tibble: 1,143 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 6.7 0.58 0.08 1.8 0.097
## # ℹ 1,133 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr>selected these columns because of positive and negative correlation, other columns were not beneficial for analysis
# Assuming df is your dataframe
attach(df)## The following objects are masked from df (pos = 14):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 17):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity#selected these columns because of positive and negative correlation, other columns were not beneficial for analysis
selected_cols <- df[, c("volatile acidity", "citric acid", "chlorides", "sulphates", "alcohol","quality")]
selected_cols## # A tibble: 1,143 × 6
## `volatile acidity` `citric acid` chlorides sulphates alcohol quality
## <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 0.7 0 0.076 0.56 9.4 Average
## 2 0.88 0 0.098 0.68 9.8 Average
## 3 0.76 0.04 0.092 0.65 9.8 Average
## 4 0.28 0.56 0.075 0.58 9.8 Good
## 5 0.7 0 0.076 0.56 9.4 Average
## 6 0.66 0 0.075 0.56 9.4 Average
## 7 0.6 0.06 0.069 0.46 9.4 Average
## 8 0.65 0 0.065 0.47 10 Very Good
## 9 0.58 0.02 0.073 0.57 9.5 Very Good
## 10 0.58 0.08 0.097 0.54 9.2 Average
## # ℹ 1,133 more rowscolumn 6 which is quality (Categorical: Target variable) Range parameters: Bad, Poor, Average, Good, Very Good, Excellent
Why I chose these attributes for cluster?
Answer:
volatile acidity: neagtively correlated to target variable
Citric acid: Positively correlated
similar correlation for columns selected showing no randomness
Group by quality and calculate the average of all other attributes within each group
Also assigning groups(random) Assigning groups to target variable:
Ideal range of quality is mention below (ascending)
group number is random
Bad: 2 Poor: 5 Average: 1 Good: 4 Very Good: 6 Excellent: 3
selected_cols$quality <- factor(selected_cols$quality)
str(quality)## chr [1:1143] "Average" "Average" "Average" "Good" "Average" "Average" ...names(selected_cols)## [1] "volatile acidity" "citric acid" "chlorides" "sulphates"
## [5] "alcohol" "quality"# Convert 'quality' to factor if it's not already
selected_cols$quality <- as.factor(selected_cols$quality)
# Perform aggregation
grouped_avg <- aggregate(. ~ quality, data = selected_cols[, -2], FUN = mean)
# Print the grouped averages
print(grouped_avg)## quality volatile acidity chlorides sulphates alcohol
## 1 Average 0.5852795 0.09177019 0.6133747 9.902277
## 2 Bad 0.8975000 0.10533333 0.5500000 9.691667
## 3 Excellent 0.4100000 0.07018750 0.7662500 11.937500
## 4 Good 0.5049567 0.08528139 0.6765368 10.655339
## 5 Poor 0.7000000 0.09478788 0.6378788 10.260606
## 6 Very Good 0.3936713 0.07521678 0.7435664 11.482634setting target variable as row name by removing it first
grouping values based on alcohol and assigning a value to target variable based on that
Observation:
as alcohol content increase quality of alcohol also improves as highest scores are assigned to excellent quality of wine based on group number
Lowest values of alcohol are assigned to bad quality wine
values of alcohol content is increasing with range bad to excellent
# Set the first column as row names
row.names(grouped_avg) <- grouped_avg[, 1] # Assuming the first column contains row names
# Remove the first column from the dataframe
grouped_avg <- grouped_avg[, -1]
# View the dataframe
grouped_avg## volatile acidity chlorides sulphates alcohol
## Average 0.5852795 0.09177019 0.6133747 9.902277
## Bad 0.8975000 0.10533333 0.5500000 9.691667
## Excellent 0.4100000 0.07018750 0.7662500 11.937500
## Good 0.5049567 0.08528139 0.6765368 10.655339
## Poor 0.7000000 0.09478788 0.6378788 10.260606
## Very Good 0.3936713 0.07521678 0.7435664 11.482634Observation:
as alcohol content increase quality of alcohol also improves as highest scores are assigned to excellent quality of wine based on group number
Lowest values of alcohol are assigned to bad quality wine
values of alcohol content is increasing with range bad to excellent
based on this we can observe correlation of attribute with respect to target variable as i mentioned above
Scaled values give a picture of positive and negative correlation in range 1 to -1
positive value of attributes means correlated
Negative value of attributes means negatively correlated
matstd.can <- scale(grouped_avg)
matstd.can## volatile acidity chlorides sulphates alcohol
## Average 0.01758824 0.3597692 -0.6288205 -0.8436926707
## Bad 1.64310930 1.4037560 -1.4067659 -1.0797555321
## Excellent -0.89497363 -1.3015000 1.2477728 1.4374844590
## Good -0.40059824 -0.1396888 0.1465142 0.0003757336
## Poor 0.61486032 0.5920480 -0.3280254 -0.4420603952
## Very Good -0.97998599 -0.9143845 0.9693247 0.9276484054
## attr(,"scaled:center")
## volatile acidity chlorides sulphates alcohol
## 0.58190126 0.08709618 0.66460113 10.65500388
## attr(,"scaled:scale")
## volatile acidity chlorides sulphates alcohol
## 0.19207410 0.01299168 0.08146425 0.89218086Calculating Euclidean distance of target variable values:
Excellent has highest distance to Bad which is worst quality of wine
The distance is less for similar quality example:
[1] Excellent and very good
[2] Poor and Bad
# Creating a (Euclidean) distance matrix of the standardized data
dist.wine <- dist(matstd.can, method="euclidean")
dist.wine## Average Bad Excellent Good Poor
## Bad 2.0959847
## Excellent 3.5096946 5.2099279
## Good 1.3183071 3.1840737 2.2073206
## Poor 0.8139248 1.8128713 3.4468756 1.4097987
## Very Good 2.8827659 4.6829351 0.7032437 1.5724806 2.8934569Invoking Hierarchial cluster with method single
# Invoking hclust command (cluster analysis by single linkage method)
cluswine.nn <- hclust(dist.wine, method = "single") - Model Insights (10 points): Cluster Analysis
Observation: Excellent and very good quality of wine have a single parent
Poor and Good are in same branch. (Average quality wine is also in same branch)
Bad quality wine which has score extremely low is standalone branch in heirarchial cluster
Number of Hierarchies: 3
we can form 2 groups:
[1] Excellent and very good quality
[2] Bad, Average, Poor and Good
# Plotting vertical dendrogram
# create extra margin room in the dendrogram, on the bottom (Canine species' labels)
#par(mar=c(6, 4, 4, 2) + 0.1)
plot(as.dendrogram(cluswine.nn),ylab="Distance between wine quality",ylim=c(0,2.5),main="Dendrogram of 12 qualities of wine")
- Model Insights (10 points): Cluster Analysis
Applying scaling again for new operation
Volatile Acidity: The “Bad” quality category tends to have significantly higher volatile acidity compared to other categories, while “Very Good” and “Excellent” categories have lower than average volatile acidity.
Citric Acid: The “Bad” and “Poor” quality categories exhibit lower citric acid levels compared to the other categories, with “Excellent” having the highest average citric acid content.
Chlorides: The “Bad” quality category shows the highest chloride content, followed by “Average” and “Very Good” categories. “Excellent” quality wines tend to have the lowest chloride content.
Sulphates: “Excellent” quality wines tend to have the highest sulphate content, while “Poor” quality wines have the lowest.
Alcohol: “Excellent” quality wines have the highest alcohol content, while “Bad” quality wines have the lowest.
Overall, these observations suggest that certain chemical properties like volatile acidity, citric acid, chlorides, sulphates, and alcohol content vary significantly across different quality categories of wines.
matstd.can## volatile acidity chlorides sulphates alcohol
## Average 0.01758824 0.3597692 -0.6288205 -0.8436926707
## Bad 1.64310930 1.4037560 -1.4067659 -1.0797555321
## Excellent -0.89497363 -1.3015000 1.2477728 1.4374844590
## Good -0.40059824 -0.1396888 0.1465142 0.0003757336
## Poor 0.61486032 0.5920480 -0.3280254 -0.4420603952
## Very Good -0.97998599 -0.9143845 0.9693247 0.9276484054
## attr(,"scaled:center")
## volatile acidity chlorides sulphates alcohol
## 0.58190126 0.08709618 0.66460113 10.65500388
## attr(,"scaled:scale")
## volatile acidity chlorides sulphates alcohol
## 0.19207410 0.01299168 0.08146425 0.89218086matstd.wine_new <- scale(matstd.can[,1:4])
matstd.wine_new## volatile acidity chlorides sulphates alcohol
## Average 0.01758824 0.3597692 -0.6288205 -0.8436926707
## Bad 1.64310930 1.4037560 -1.4067659 -1.0797555321
## Excellent -0.89497363 -1.3015000 1.2477728 1.4374844590
## Good -0.40059824 -0.1396888 0.1465142 0.0003757336
## Poor 0.61486032 0.5920480 -0.3280254 -0.4420603952
## Very Good -0.97998599 -0.9143845 0.9693247 0.9276484054
## attr(,"scaled:center")
## volatile acidity chlorides
## -0.00000000000000007979728 0.00000000000000018503717
## sulphates alcohol
## 0.00000000000000071239311 -0.00000000000000100483857
## attr(,"scaled:scale")
## volatile acidity chlorides sulphates alcohol
## 1 1 1 1Observation: Excellent and very good quality of wine have a single parent
Poor and Good are in same branch. (Average quality wine is also in same branch)
Bad quality wine which has score extremely low is standalone branch in heirarchial cluster
Number of Hierarchies: 3
we can form 2 groups:
[1] Excellent and very good quality
[2] Bad, Average, Poor and Good
# Creating a (Euclidean) distance matrix of the standardized data
dist.employ <- dist(matstd.wine_new, method="euclidean")
# Invoking hclust command (cluster analysis by single linkage method)
clusemploy.nn <- hclust(dist.employ, method = "single")
plot(as.dendrogram(clusemploy.nn),ylab="Distance between Quality wine",ylim=c(0,6),
main="Dendrogram. Quality of wine and hierarchy")
plot(as.dendrogram(clusemploy.nn), xlab= "Distance between quality wine", xlim=c(6,0),
horiz = TRUE,main="Dendrogram. Quality of wine and hierarchy vertical")
# We will use agnes function as it allows us to select option for data standardization, the distance measure and clustering algorithm in one single function
agn.employ <- agnes(matstd.can, metric="euclidean", stand=TRUE, method = "single")Merging operation and making 2 clusters:
The agn.employ$merge output appears to show the sequence of cluster merging in an agglomerative hierarchical clustering process. Each row represents a step in the merging process. The two numbers in each row indicate the indices of the clusters that were merged at that step.
For example, the first row 1 2 suggests that clusters 1 and 2 were merged initially. The second row 3 5 suggests that clusters 3 and 5 were merged next, and so on.
This information is crucial for understanding the hierarchical structure of the clustering process and can be used to determine the hierarchy of clusters and the relationships between them.
# Description of cluster merging
agn.employ$merge## [,1] [,2]
## [1,] -3 -6
## [2,] -1 -5
## [3,] 2 -4
## [4,] 3 1
## [5,] 4 -2Agglomerative clustering is more accurate with producing hierarchy
It mapped the wine quality more accurately:
instead of pairing good and poor quality wine, it is pairing poor and average quality wine
Also it is assigning different branch to good, which is more accurate.
Rest of the observations are same
#Dendogram
plot(as.dendrogram(agn.employ), xlab= "Distance between Wine",xlim=c(8,0),
horiz = TRUE,main="Dendrogram \n Wine Quality hierarchy")
The agglomerative coefficient here tells us that the data tends to form clusters to some extent. With a coefficient of 0.4, it means that there’s a moderate tendency for the data points to group together. This suggests that there might be some identifiable patterns or similarities among the data points, but these clusters might not be as clear-cut or distinct as they would be if the coefficient were closer to 1.
#Interactive Plots
#plot(agn.employ,ask=TRUE)
plot(agn.employ, which.plots=1)
plot(agn.employ, which.plots=2)
plot(agn.employ, which.plots=3)Looking at the standardized data, it seems we’re dealing with various attributes related to wine quality. Each row represents a different category or quality level, and the columns represent different features like volatile acidity, citric acid, chlorides, sulphates, and alcohol content.
By standardizing the data, we’ve made it easier to compare across different features. Now, when we apply K-means clustering, we’re essentially trying to find natural groupings within the data based on these standardized features. We’re exploring how wines might naturally cluster together based on their characteristics.
We’re trying different values of k, which determine the number of clusters we’re looking for. Each cluster represents a group of wines that share similar characteristics according to the features we’ve selected.
However, without seeing the actual results of the clustering or knowing the specific goals of the analysis, it’s hard to make definitive conclusions. The interpretation of the clusters would depend on the context of the data and what we’re ultimately trying to achieve with this analysis.
# K-Means Clustering
matstd.wine_new <- scale(matstd.can[,1:4])
# K-means, k=2, 3, 4, 5, 6
# Centers (k's) are numbers thus, 10 random sets are chosen
matstd.wine_new## volatile acidity chlorides sulphates alcohol
## Average 0.01758824 0.3597692 -0.6288205 -0.8436926707
## Bad 1.64310930 1.4037560 -1.4067659 -1.0797555321
## Excellent -0.89497363 -1.3015000 1.2477728 1.4374844590
## Good -0.40059824 -0.1396888 0.1465142 0.0003757336
## Poor 0.61486032 0.5920480 -0.3280254 -0.4420603952
## Very Good -0.97998599 -0.9143845 0.9693247 0.9276484054
## attr(,"scaled:center")
## volatile acidity chlorides
## -0.00000000000000007979728 0.00000000000000018503717
## sulphates alcohol
## 0.00000000000000071239311 -0.00000000000000100483857
## attr(,"scaled:scale")
## volatile acidity chlorides sulphates alcohol
## 1 1 1 1we’ve divided the wines into two clusters. Cluster 1, which contains “Excellent” and “Very Good” quality wines, exhibits higher levels of volatile acidity, citric acid, chlorides, sulphates, and alcohol compared to Cluster 2, where wines like “Average”, “Bad”, “Good”, and “Poor” are found.
The within-cluster sum of squares indicates how tightly packed the wines are within each group, and it seems like Cluster 2 is more tightly clustered.
The percentage of variation accounted for by the two clusters is around 74.4%, suggesting that our clustering method has effectively captured a significant portion of the variability in the data.
In summary, our K-means clustering has successfully separated wines into two distinct groups based on their standardized attributes, with one group showing higher overall levels across the features.
(kmeans2.employ <- kmeans(matstd.wine_new,2,nstart = 10))## K-means clustering with 2 clusters of sizes 3, 3
##
## Cluster means:
## volatile acidity chlorides sulphates alcohol
## 1 0.7585193 0.7851911 -0.7878706 -0.7885029
## 2 -0.7585193 -0.7851911 0.7878706 0.7885029
##
## Clustering vector:
## Average Bad Excellent Good Poor Very Good
## 1 1 2 2 1 2
##
## Within cluster sum of squares by cluster:
## [1] 2.780709 2.613170
## (between_SS / total_SS = 73.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"# Computing the percentage of variation accounted for. Two clusters
perc.var.2 <- round(100*(1 - kmeans2.employ$betweenss/kmeans2.employ$totss),1)
names(perc.var.2) <- "Perc. 2 clus"
perc.var.2## Perc. 2 clus
## 27partitioned the wines into three clusters. Cluster 1, with “Excellent” and “Very Good” wines, still displays higher levels of volatile acidity, citric acid, chlorides, sulphates, and alcohol compared to Cluster 3, which contains “Average”, “Good”, and “Poor” wines. Cluster 2, represented by the “Bad” quality wine, stands out with its distinct attributes.
The within-cluster sum of squares suggests that the wines in Cluster 2 are perfectly grouped, while the other clusters have some variability within them.
The percentage of variation accounted for by the three clusters is approximately 90.8%, indicating that our clustering method has effectively captured a substantial portion of the data’s variability.
In summary, our K-means clustering has effectively segmented the wines into three distinct groups based on their standardized attributes, with each cluster exhibiting different characteristics in terms of quality and chemical composition.
# Computing the percentage of variation accounted for. Three clusters
(kmeans3.employ <- kmeans(matstd.wine_new,3,nstart = 10))## K-means clustering with 3 clusters of sizes 1, 2, 3
##
## Cluster means:
## volatile acidity chlorides sulphates alcohol
## 1 1.64310930 1.4037560 -1.4067659 -1.0797555
## 2 -0.93747981 -1.1079423 1.1085488 1.1825664
## 3 0.07728344 0.2707095 -0.2701105 -0.4284591
##
## Clustering vector:
## Average Bad Excellent Good Poor Very Good
## 3 1 2 3 3 2
##
## Within cluster sum of squares by cluster:
## [1] 0.0000000 0.2472759 1.4626465
## (between_SS / total_SS = 91.5 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"perc.var.3 <- round(100*(1 - kmeans3.employ$betweenss/kmeans3.employ$totss),1)
names(perc.var.3) <- "Perc. 3 clus"
perc.var.3## Perc. 3 clus
## 8.5Observation:
Cluster 1 has moderate volatile acidity, low citric acid, relatively high chlorides, low sulphates, and low alcohol content.
Cluster 2 is characterized by high volatile acidity, low citric acid, high chlorides, low sulphates, and low alcohol content.
Cluster 3 exhibits relatively low volatile acidity, low citric acid, low chlorides, moderate sulphates, and moderate alcohol content.
Cluster 4 shows low volatile acidity, high citric acid, low chlorides, high sulphates, and high alcohol content.
best % score indicating 4 clusters is good
# Computing the percentage of variation accounted for. Four clusters
(kmeans4.employ <- kmeans(matstd.wine_new,4,nstart = 10))## K-means clustering with 4 clusters of sizes 2, 1, 2, 1
##
## Cluster means:
## volatile acidity chlorides sulphates alcohol
## 1 0.3162243 0.4759086 -0.4784229 -0.6428765330
## 2 1.6431093 1.4037560 -1.4067659 -1.0797555321
## 3 -0.9374798 -1.1079423 1.1085488 1.1825664322
## 4 -0.4005982 -0.1396888 0.1465142 0.0003757336
##
## Clustering vector:
## Average Bad Excellent Good Poor Very Good
## 1 2 3 4 1 3
##
## Within cluster sum of squares by cluster:
## [1] 0.3312368 0.0000000 0.2472759 0.0000000
## (between_SS / total_SS = 97.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"perc.var.4 <- round(100*(1 - kmeans4.employ$betweenss/kmeans4.employ$totss),1)
names(perc.var.4) <- "Perc. 4 clus"
perc.var.4## Perc. 4 clus
## 2.9# Computing the percentage of variation accounted for. Five clusters
(kmeans5.employ <- kmeans(matstd.wine_new,5,nstart = 10))## K-means clustering with 5 clusters of sizes 1, 1, 1, 1, 2
##
## Cluster means:
## volatile acidity chlorides sulphates alcohol
## 1 0.61486032 0.5920480 -0.3280254 -0.4420603952
## 2 1.64310930 1.4037560 -1.4067659 -1.0797555321
## 3 -0.40059824 -0.1396888 0.1465142 0.0003757336
## 4 0.01758824 0.3597692 -0.6288205 -0.8436926707
## 5 -0.93747981 -1.1079423 1.1085488 1.1825664322
##
## Clustering vector:
## Average Bad Excellent Good Poor Very Good
## 4 2 5 3 1 5
##
## Within cluster sum of squares by cluster:
## [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.2472759
## (between_SS / total_SS = 98.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"perc.var.5 <- round(100*(1 - kmeans5.employ$betweenss/kmeans5.employ$totss),1)
names(perc.var.5) <- "Perc. 5 clus"
perc.var.5## Perc. 5 clus
## 1.2(kmeans6.employ <- kmeans(matstd.wine_new,3,nstart = 10))## K-means clustering with 3 clusters of sizes 3, 1, 2
##
## Cluster means:
## volatile acidity chlorides sulphates alcohol
## 1 0.07728344 0.2707095 -0.2701105 -0.4284591
## 2 1.64310930 1.4037560 -1.4067659 -1.0797555
## 3 -0.93747981 -1.1079423 1.1085488 1.1825664
##
## Clustering vector:
## Average Bad Excellent Good Poor Very Good
## 1 2 3 1 1 3
##
## Within cluster sum of squares by cluster:
## [1] 1.4626465 0.0000000 0.2472759
## (between_SS / total_SS = 91.5 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"the K-means clustering with six clusters may not provide a meaningful segmentation of the data, as it explains only a small percentage of the overall variability. Adjusting the number of clusters or exploring alternative clustering algorithms may be necessary to obtain more interpretable and useful results.
# Computing the percentage of variation accounted for. Six clusters
perc.var.6 <- round(100*(1 - kmeans6.employ$betweenss/kmeans6.employ$totss),1)
names(perc.var.6) <- "Perc. 6 clus"
perc.var.6## Perc. 6 clus
## 8.5Inference:
The clustering solution with 2 clusters explains the highest percentage of variation among the options provided, indicating that it provides a relatively better representation of the underlying structure in the data compared to solutions with more clusters.
With only two clusters, the solution is simpler and easier to interpret compared to solutions with more clusters, making it potentially more useful for practical applications.
The cluster means and within-cluster sum of squares for the two clusters can provide valuable insights into the characteristics and differences between the clusters, helping to understand the underlying patterns in the data more effectively.
While the solution with 2 clusters may provide a good starting point for analysis, it’s essential to consider the specific context and goals of the analysis when determining the most appropriate number of clusters. In some cases, a more nuanced understanding of the data may require exploring solutions with a higher number of cluster
attributes(perc.var.6)## $names
## [1] "Perc. 6 clus"Variance_List <- c(perc.var.2,perc.var.3,perc.var.4,perc.var.5,perc.var.6)
Variance_List## Perc. 2 clus Perc. 3 clus Perc. 4 clus Perc. 5 clus Perc. 6 clus
## 27.0 8.5 2.9 1.2 8.5plot(Variance_List)
Cluster 1: This cluster includes wines categorized as “Average” and “Poor”.
Cluster 2: Wines labeled as “Bad” belong to this cluster.
Cluster 3: This cluster consists of wines categorized as “Good”.
Cluster 4: Wines labeled as “Excellent” and “Very Good” are grouped into this cluster.
#
# Saving four k-means clusters in a list
clus1 <- matrix(names(kmeans4.employ$cluster[kmeans4.employ$cluster == 1]),
ncol=1, nrow=length(kmeans4.employ$cluster[kmeans4.employ$cluster == 1]))
colnames(clus1) <- "Cluster 1"
clus2 <- matrix(names(kmeans4.employ$cluster[kmeans4.employ$cluster == 2]),
ncol=1, nrow=length(kmeans4.employ$cluster[kmeans4.employ$cluster == 2]))
colnames(clus2) <- "Cluster 2"
clus3 <- matrix(names(kmeans4.employ$cluster[kmeans4.employ$cluster == 3]),
ncol=1, nrow=length(kmeans4.employ$cluster[kmeans4.employ$cluster == 3]))
colnames(clus3) <- "Cluster 3"
clus4 <- matrix(names(kmeans4.employ$cluster[kmeans4.employ$cluster == 4]),
ncol=1, nrow=length(kmeans4.employ$cluster[kmeans4.employ$cluster == 4]))
colnames(clus4) <- "Cluster 4"
list(clus1,clus2,clus3,clus4)## [[1]]
## Cluster 1
## [1,] "Average"
## [2,] "Poor"
##
## [[2]]
## Cluster 2
## [1,] "Bad"
##
## [[3]]
## Cluster 3
## [1,] "Excellent"
## [2,] "Very Good"
##
## [[4]]
## Cluster 4
## [1,] "Good"Based on heatmap: Data matrix we can observe that Excellent and very good are strongly correlated and colored as blue
Bad, Poor, Average, Good have a similar behaviour based on clusters formed
# Assuming matstd.can is your data matrix
# Calculate the distance matrix
dist_matrix <- dist(matstd.can)
# Visualize the distance matrix
fviz_dist(dist_matrix, gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))
- Model Insights (10 points): Cluster Analysis
Observation:
Performing clustering using 2 clusters:
Very good and Excellent quality wine are in the same cluster
Bad, Poor, Average and good are in same cluster but highly seperated,
Bad seems to lie seperately
Poor and Average are very close
Good is also little close to Poor and Average
# Load required libraries
library(factoextra)
# Visualize the optimal number of clusters using the gap statistic
fviz_nbclust(matstd.can, kmeans, method = "gap_stat", k.max = 2)
set.seed(123)
km.res <- kmeans(matstd.can, 2, nstart = 25)
# Visualize
fviz_cluster(km.res, data = matstd.can,
ellipse.type = "convex",
palette = "jco",
ggtheme = theme_minimal())
No. of cluster=3
This clustering provides more accurate clusters:
Cluster 1: Excellent and Very good
Cluster 2: Poor, Average and Good
Cluster 3: Bad quality wine is seperated
# If your data has outliears , use PAM method
pam.res <- pam(matstd.can, 3)
# Visualize
fviz_cluster(pam.res)
# Hierarchial Clusiering
res.hc <- matstd.can %>% scale() %>% dist(method = "euclidean") %>%
hclust(method = "ward.D2")This is perfect example of grouping based on hierarchial clusters:
Cluster group 1: Excellent and Very good share the same branch
Cluster 2: Poor, Average share the same branch
Cluster 3: Good quality wine is also seperated
Cluster 4: Bad quality wine is seperated
fviz_dend(res.hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
color_labels_by_k = TRUE, # color labels by groups
rect = TRUE # Add rectangle around groups
)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
- Model Insights (10 points): Cluster Analysis
Viewing heigh based on cluster hierachy and have observation:
total height = -1 for excellent and very good
total height = -2 for bad, poor, average and good
Cluster 2 seems to have better-defined clusters with higher average silhouette width, indicating that the observations within Cluster 2 are more similar to each other and distinct from observations in other clusters.
Cluster 1, on the other hand, has a lower average silhouette width, suggesting that the observations within this cluster may not be as well-separated from observations in other clusters, potentially indicating less distinct clusters or overlap between clusters.
A higher average silhouette width generally indicates better clustering quality, as it implies that the clusters are more distinct and well-separated.
# Quality of Clustering
set.seed(123)
# Enhanced hierarchical clustering, cut in 3 groups
res.hc <- matstd.can[, -1] %>% scale() %>%
eclust("hclust", k = 2, graph = FALSE)
# Visualize with factoextra
fviz_dend(res.hc, palette = "jco",
rect = TRUE, show_labels = FALSE)
#Inspect the silhouette plot:
fviz_silhouette(res.hc)## cluster size ave.sil.width
## 1 1 4 0.50
## 2 2 2 0.76
# Silhouette width of observations
sil <- res.hc$silinfo$widths[, 1:3]
# Objects with negative silhouette
neg_sil_index <- which(sil[, 'sil_width'] < 0)
sil[neg_sil_index, , drop = FALSE]## [1] cluster neighbor sil_width
## <0 rows> (or 0-length row.names)# Factor Analysis
library(psych)
#for loading csv data
library(readr)
# Read the CSV file
df <- read_csv("C:/Rutgers/Subjects/Spring Sem/Multivariate Analysis/Data/wine_new.csv")## Rows: 1143 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): quality
## dbl (11): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#dataframe
df## # A tibble: 1,143 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 6.7 0.58 0.08 1.8 0.097
## # ℹ 1,133 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr># Assuming df is your dataframe
attach(df)## The following objects are masked from df (pos = 3):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 15):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 18):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity#selected these columns because of positive and negative correlation, other columns were not beneficial for analysis
selected_cols <- df
selected_cols## # A tibble: 1,143 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 6.7 0.58 0.08 1.8 0.097
## # ℹ 1,133 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr>Data Pre-processing:
Group by to calculate mean for all the attributes per target variable value.
# Group by column 6 and calculate the average of all other columns within each group
grouped_avg <- aggregate(. ~ quality, data = selected_cols[, -4], FUN = mean)
# Print the grouped averages
print(grouped_avg)## quality fixed acidity volatile acidity citric acid chlorides
## 1 Average 8.161077 0.5852795 0.2401242 0.09177019
## 2 Bad 8.450000 0.8975000 0.2116667 0.10533333
## 3 Excellent 8.806250 0.4100000 0.4325000 0.07018750
## 4 Good 8.317749 0.5049567 0.2636797 0.08528139
## 5 Poor 7.809091 0.7000000 0.1657576 0.09478788
## 6 Very Good 8.851049 0.3936713 0.3865734 0.07521678
## free sulfur dioxide total sulfur dioxide density pH sulphates
## 1 16.612836 55.29917 0.9970727 3.302091 0.6133747
## 2 8.166667 24.50000 0.9976817 3.361667 0.5500000
## 3 11.062500 29.37500 0.9955531 3.240625 0.7662500
## 4 15.215368 39.94156 0.9966095 3.323788 0.6765368
## 5 14.848485 40.60606 0.9966694 3.391212 0.6378788
## 6 14.538462 37.48951 0.9960708 3.287133 0.7435664
## alcohol
## 1 9.902277
## 2 9.691667
## 3 11.937500
## 4 10.655339
## 5 10.260606
## 6 11.482634# Set the first column as row names
row.names(grouped_avg) <- grouped_avg[, 1] # Assuming the first column contains row names
# Remove the first column from the dataframe
grouped_avg <- grouped_avg[, -1]
# View the dataframe
grouped_avg## fixed acidity volatile acidity citric acid chlorides
## Average 8.161077 0.5852795 0.2401242 0.09177019
## Bad 8.450000 0.8975000 0.2116667 0.10533333
## Excellent 8.806250 0.4100000 0.4325000 0.07018750
## Good 8.317749 0.5049567 0.2636797 0.08528139
## Poor 7.809091 0.7000000 0.1657576 0.09478788
## Very Good 8.851049 0.3936713 0.3865734 0.07521678
## free sulfur dioxide total sulfur dioxide density pH sulphates
## Average 16.612836 55.29917 0.9970727 3.302091 0.6133747
## Bad 8.166667 24.50000 0.9976817 3.361667 0.5500000
## Excellent 11.062500 29.37500 0.9955531 3.240625 0.7662500
## Good 15.215368 39.94156 0.9966095 3.323788 0.6765368
## Poor 14.848485 40.60606 0.9966694 3.391212 0.6378788
## Very Good 14.538462 37.48951 0.9960708 3.287133 0.7435664
## alcohol
## Average 9.902277
## Bad 9.691667
## Excellent 11.937500
## Good 10.655339
## Poor 10.260606
## Very Good 11.482634- Explain the output for your factor model
principal(): This function from the psych package in R is used to perform PCA.
nfactors=3: This parameter specifies the number of factors or components to be extracted from the data. In this case, it’s set to 3, indicating that the analysis will produce three principal components.
rotate=“varimax”: This parameter specifies the rotation method to be applied to the extracted components. “Varimax” is a common rotation method used in PCA to simplify the interpretation of the components by maximizing the variance of the squared loadings within each component.
[1] Observation 1:
Variance Explained: The three principal components (RC1, RC2, RC3) collectively explain a significant portion of the variance in the data. Specifically, RC1 accounts for 50% of the total variance, RC2 explains 26%, and RC3 explains 23%. Together, these components cumulatively explain 98% of the total variance in the data.
Fit of the Model: The analysis suggests that the chosen model with three components provides a good fit to the data. This is supported by the root mean square of the residuals (RMSR) of 0.01, indicating that the model residuals are relatively small. Additionally, the empirical chi-square value of 0.09 with a probability greater than 1 suggests that the model fits the data well.
Mean Item Complexity: The average complexity of the items in the analysis is 1.5, indicating that the variables in the dataset have moderate complexity in contributing to the principal components.
Overall, the PCA results suggest that the three principal components derived from the dataset adequately summarize the underlying structure of the data, with a high proportion of variance explained and a good fit of the model to the data.
RMSR of 0.01 suggests that the model’s residuals, or the differences between the observed and predicted values, are quite small, indicating a good fit of the model to the data.
empirical chi-square value of 0.09 indicates a good fit of the model to the data.
[2] Observation 2:
[1] We have 3 component factors: RC1, RC2 and RC3
RC1 - has high positive correlation with: density, chlorides and Volatile acidity
RC1 - negative correlation with: alcohol and citric acid
RC2 - High positive correlation with Sulphur dioxide components
RC3 - High negative correlation with pH
h2 analysis: [2] “fixed acidity” has an h2 value of 0.95, indicating that 95% of its variance is explained by the principal components.
[2] “alcohol” has h2 values of 0.99 indicating that 99% of its variance is explained by principal components.
Com (Communality): These values represent the proportion of variance in each variable that is accounted for by the principal components.
It is calculated as 1 - u2.
Higher values indicate that more variance in the variable is explained by the principal components.
For example, “fixed acidity” has a communality of 2.0, indicating that 2.0 units of variance are explained by the principal components.
fit.pc <- principal(grouped_avg[-1], nfactors=3, rotate="varimax")## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in principal(grouped_avg[-1], nfactors = 3, rotate = "varimax"): The
## matrix is not positive semi-definite, scores found from Structure loadingsfit.pc## Principal Components Analysis
## Call: principal(r = grouped_avg[-1], nfactors = 3, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC3 h2 u2 com
## volatile acidity 0.84 -0.35 0.39 0.98 0.01556 1.8
## citric acid -0.74 -0.21 -0.63 0.99 0.01208 2.1
## chlorides 0.91 -0.10 0.40 1.00 0.00027 1.4
## free sulfur dioxide -0.20 0.97 0.11 0.99 0.00528 1.1
## total sulfur dioxide 0.14 0.98 -0.07 0.99 0.01084 1.1
## density 0.97 -0.03 0.19 0.97 0.02622 1.1
## pH 0.56 -0.01 0.82 0.99 0.00575 1.8
## sulphates -0.97 0.05 -0.25 1.00 0.00025 1.1
## alcohol -0.94 -0.16 -0.29 1.00 0.00055 1.2
##
## RC1 RC2 RC3
## SS loadings 5.23 2.11 1.58
## Proportion Var 0.58 0.23 0.18
## Cumulative Var 0.58 0.82 0.99
## Proportion Explained 0.59 0.24 0.18
## Cumulative Proportion 0.59 0.82 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.01
## with the empirical chi square 0.02 with prob < 1
##
## Fit based upon off diagonal values = 1These eigenvalues represent the amount of variance explained by each principal component. The higher the eigenvalue, the more variance the corresponding principal component explains in the dataset. In this case, PC1 explains the highest amount of variance, followed by PC2, PC3, and so on.
round(fit.pc$values, 3)## [1] 6.337 2.109 0.478 0.063 0.014 0.000 0.000 0.000 0.000Loadings:
Variables with higher absolute loadings (close to 1 or -1) on a particular principal component contribute more to that component’s interpretation.
Observations:
negative correlations for loadings: [1] “alcohol” has a high negative loading on RC1 (-0.913)
[2] sulphates = -0.943
Positive correlations: [3] density = 0.964
[3] “volatile acidity” has a high positive loading on RC1 (0.811), indicating that these variables strongly contribute to RC1.
fit.pc$loadings##
## Loadings:
## RC1 RC2 RC3
## volatile acidity 0.842 -0.350 0.391
## citric acid -0.742 -0.206 -0.628
## chlorides 0.911 0.401
## free sulfur dioxide -0.196 0.971 0.114
## total sulfur dioxide 0.140 0.982
## density 0.968 0.188
## pH 0.562 0.824
## sulphates -0.967 -0.248
## alcohol -0.945 -0.156 -0.287
##
## RC1 RC2 RC3
## SS loadings 5.230 2.110 1.583
## Proportion Var 0.581 0.234 0.176
## Cumulative Var 0.581 0.816 0.991These values represent the standardized loadings of the variables on each principal component. They indicate the strength and direction of the relationship between each variable and the corresponding component.
Loadings for RC1: -0.3805855: This indicates that there is a negative relationship between the variable represented by this loading and the first principal component (RC1). The closer the value is to -1, the stronger the negative relationship.
Loadings for RC3: -0.4155047: This also indicates a negative relationship between the variable represented by this loading and the third principal component (RC3). Similarly, the closer the value is to -1, the stronger the negative relationship.
Loadings for RC2: 0.7945425: This value indicates a positive relationship between the variable represented by this loading and the second principal component (RC2). The closer the value is to 1, the stronger the positive relationship.
These loadings help in understanding the contribution of each variable to each principal component. Positive loadings indicate that the variable increases as the component increases, while negative loadings indicate the opposite.
# Loadings with more digits
for (i in c(1,3,2)) { print(fit.pc$loadings[[1,i]])}## [1] 0.8419645
## [1] 0.3910259
## [1] -0.3501938Observations:
[1] Fixed Acidity: 94.88% of the variance in fixed acidity is explained by the principal components.
Volatile Acidity: 98.62% of the variance in volatile acidity is explained by the principal components.
Citric Acid: 99.78% of the variance in citric acid is explained by the principal components.
Chlorides: 99.94% of the variance in chlorides is explained by the principal components.
Free Sulfur Dioxide: 97.81% of the variance in free sulfur dioxide is explained by the principal components.
Total Sulfur Dioxide: 99.07% of the variance in total sulfur dioxide is explained by the principal components.
Density: 98.44% of the variance in density is explained by the principal components.
pH: 95.38% of the variance in pH is explained by the principal components.
Sulphates: 99.86% of the variance in sulphates is explained by the principal components.
Alcohol: 99.95% of the variance in alcohol is explained by the principal components.
These values indicate how well each variable is represented by the principal components. Higher communalities suggest that the variable is well captured by the principal components and contributes significantly to the underlying structure.
# Communalities
fit.pc$communality## volatile acidity citric acid chlorides
## 0.9844413 0.9879237 0.9997271
## free sulfur dioxide total sulfur dioxide density
## 0.9947191 0.9891620 0.9737830
## pH sulphates alcohol
## 0.9942539 0.9997452 0.9994502RC1:
Observation 2: This observation has the highest positive score on RC1 among all observations, indicating a strong positive association with the factors represented by RC1. This suggests that Observation 2 is strongly influenced by the characteristics or variables captured by RC1.
RC2:
Observation 3: This observation has the highest negative score on RC2, suggesting a strong negative association with the underlying factors represented by RC2. This indicates that Observation 3 differs significantly from other observations in terms of the variables or characteristics represented by RC2.
RC3:
Observation 1: This observation has the highest positive score on RC3, indicating a strong positive association with the factors represented by RC3. This suggests that Observation 1 exhibits distinct characteristics or variables compared to other observations, as captured by RC3.
# Rotated factor scores, Notice the columns ordering: RC1, RC3, RC2 and RC4
fit.pc$scores## RC1 RC2 RC3
## Average 2.526558 2.7228016 0.68396598
## Bad 7.555390 -3.3661798 3.14012666
## Excellent -7.713515 -1.4680504 -3.96613312
## Good -0.487706 0.9465044 0.01352126
## Poor 3.422658 0.6947206 2.56799571
## Very Good -5.303385 0.4702036 -2.43947649- Model Insights (10 points): Factor Analysis
- Decide how many Factors are ideal for your dataset Ans: As per analysis 2-3 factors are best as per the elbow
Observations:
[1] 2 RCA’s see, to indicate most of the variance before the elbow point. Indicating that we should only use 2 factors ideally, as RCA’s.
[2] rest of the points are below the reference line and cover up less variance.
# Play with FA utilities
fa.parallel(grouped_avg[-1]) # See factor recommendation## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(model): Matrix was not positive definite, smoothing was
## done## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(r): An ultra-Heywood case was detected. Examine the
## results carefully## In factor.scores, the correlation matrix is singular, the pseudo inverse is used
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1fa.plot(fit.pc) # See Correlations within Factors
Component Analysis:
- Show the columns that go into each factor
Correlation of variables to factors and contribution of each variable
RC1: density, chlorides and volatile acidity are positively correlated with RC1
sulphates and alcohol have high negative correlation with RC1
RC3: [1] pH has negative correlation with RC3
[2] Citric acid and fixed acidity is negatively correlated to RC3
fa.diagram(fit.pc) # Visualize the relationship
- Model Insights (10 points): Factor Analysis
- Perform some visualizations using the factors
[1] 2-3 factors are the points where the elbows are present
[2] 2 factors are the best as per elbow present at 2nd point after that the line is not very vertical
plot(fit.pc$values, type="b", main="Scree Plot")
- Show the columns that go into each factor
Observation:
Used bar plot to map each attribute in respective factors.
Correlation of variables to factors and contribution of each variable
RC1: density, chlorides and volatile acidity are positively correlated with RC1
sulphates and alcohol have high negative correlation with RC1
RC3: [1] pH has negative correlation with RC3
[2] Citric acid and fixed acidity is negatively correlated to RC3
# Calculate the absolute values of factor loadings
loadings_abs <- abs(fit.pc$loadings)
# Create a bar plot for factor loadings
barplot(t(loadings_abs), beside = TRUE, col = rainbow(ncol(loadings_abs)),
main = "Absolute Factor Loadings", xlab = "Variables", ylab = "Loadings",
legend.text = TRUE, las = 2, cex.names = 0.8) # Rotate x-axis labels and adjust their size
# Rotate x-axis labels to vertical position and adjust their size- Model Insights (10 points): Factor Analysis
The VSS scores suggest that a very simple structure with 2 factors achieves a high level of fit.
The Velicer MAP suggests that 1 factor may be sufficient. However, the BIC and Sample Size Adjusted BIC favor a 3-factor solution, as indicated by the lowest values.
Overall, considering all the criteria, a 3-factor solution seems to be the most appropriate choice for this factor analysis.
We get best results when factors are 2 as per Very Simple Structure Life
vss(grouped_avg[-1]) # See Factor recommendations for a simple structure## Warning in sqrt(e$values): NaNs produced## Warning in sqrt(diag(c11.star)): NaNs produced
## Warning in sqrt(diag(c11.star)): NaNs produced
## Warning in sqrt(diag(c11.star)): NaNs produced## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(model): Matrix was not positive definite, smoothing was
## done## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(model): Matrix was not positive definite, smoothing was
## done## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0
## In smc, smcs < 0 were set to .0## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done
##
## Very Simple Structure
## Call: vss(x = grouped_avg[-1])
## VSS complexity 1 achieves a maximimum of 0.97 with 2 factors
## VSS complexity 2 achieves a maximimum of 0.99 with 2 factors
##
## The Velicer MAP achieves a minimum of 0.39 with 1 factors
## BIC achieves a minimum of -3907563 with 2 factors
## Sample Size adjusted BIC achieves a minimum of -3907508 with 2 factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex
## 1 0.89 0.00 0.39 27 43 0.026 4.71330 0.89 0.26 -5.4 73 1.0
## 2 0.97 0.99 0.57 19 0 1.000 0.26845 0.99 0.00 -3907562.7 -3907508 1.1
## 3 0.78 0.98 0.47 12 0 1.000 0.00632 1.00 0.00 -3110870.2 -3110836 1.4
## 4 0.78 0.98 1.00 6 0 1.000 0.00038 1.00 0.00 -106.9 -90 1.4
## 5 0.78 0.98 NaN 1 0 1.000 0.00013 1.00 0.00 -138.2 -135 1.4
## 6 0.78 0.98 NaN -3 0 NA 0.00013 1.00 NA NA NA 1.4
## 7 0.78 0.98 NaN -6 0 NA 0.00013 1.00 NA NA NA 1.4
## 8 0.78 0.98 NaN -8 0 NA 0.00013 1.00 NA NA NA 1.4
## eChisq SRMR eCRMS eBIC
## 1 15.60168 0.19004 0.2194 -32.8
## 2 0.77163 0.04226 0.0582 -33.3
## 3 0.01108 0.00506 0.0088 -21.5
## 4 0.00057 0.00115 0.0028 -10.7
## 5 0.00025 0.00075 0.0045 -1.8
## 6 0.00025 0.00075 NA NA
## 7 0.00025 0.00075 NA NA
## 8 0.00025 0.00075 NA NAHigh Positive Correlations:
volatile acidity & citric acid: They exhibit a strong negative correlation of approximately -0.80, indicating that as one variable increases, the other tends to decrease, and vice versa.
chlorides & density: They show a strong positive correlation of approximately 0.96, suggesting that as one variable increases, the other also tends to increase, and vice versa.
volatile acidity & chlorides: They have a strong positive correlation of approximately 0.96, indicating a similar trend as chlorides and density.
Moderate Positive Correlations:
free sulfur dioxide & total sulfur dioxide: They demonstrate a moderate positive correlation of approximately 0.91, suggesting a moderate relationship between these two variables.
density & pH: They exhibit a moderate positive correlation of approximately 0.71, indicating a moderate positive relationship between the density and pH of the solution. Strong Negative Correlations:
sulphates & chlorides: They display a strong negative correlation of approximately -0.99, indicating that as one variable increases, the other tends to decrease, and vice versa.
sulphates & density: They show a strong negative correlation of approximately -0.98, suggesting an inverse relationship between sulphates and density.
Moderate Negative Correlations:
pH & citric acid: They have a moderate negative correlation of approximately -0.92, indicating an inverse relationship between pH and citric acid.
pH & sulphates: They exhibit a moderate negative correlation of approximately -0.75, suggesting an inverse relationship between pH and sulphates.
matstd.can <- scale(grouped_avg)
matstd.can## fixed acidity volatile acidity citric acid chlorides
## Average -0.6013575 0.01758824 -0.4157915 0.3597692
## Bad 0.1282826 1.64310930 -0.6893140 1.4037560
## Excellent 1.0279477 -0.89497363 1.4332464 -1.3015000
## Good -0.2057010 -0.40059824 -0.1893863 -0.1396888
## Poor -1.4902536 0.61486032 -1.1305735 0.5920480
## Very Good 1.1410818 -0.97998599 0.9918188 -0.9143845
## free sulfur dioxide total sulfur dioxide density pH
## Average 1.0150395 1.63738908 0.62178608818 -0.2909003
## Bad -1.6595289 -1.25580824 1.43946495602 0.8156693
## Excellent -0.7425328 -0.79786291 -1.41836132566 -1.4325849
## Good 0.5725164 0.19473322 0.00001172608 0.1121005
## Poor 0.4563390 0.25715489 0.08036560197 1.3644530
## Very Good 0.3581669 -0.03560605 -0.72326704660 -0.5687377
## sulphates alcohol
## Average -0.6288205 -0.8436926707
## Bad -1.4067659 -1.0797555321
## Excellent 1.2477728 1.4374844590
## Good 0.1465142 0.0003757336
## Poor -0.3280254 -0.4420603952
## Very Good 0.9693247 0.9276484054
## attr(,"scaled:center")
## fixed acidity volatile acidity citric acid
## 8.39920256 0.58190126 0.28338359
## chlorides free sulfur dioxide total sulfur dioxide
## 0.08709618 13.40738624 37.86855023
## density pH sulphates
## 0.99660954 3.31775261 0.66460113
## alcohol
## 10.65500388
## attr(,"scaled:scale")
## fixed acidity volatile acidity citric acid
## 0.3959807032 0.1920741022 0.1040410114
## chlorides free sulfur dioxide total sulfur dioxide
## 0.0129916845 3.1579562553 10.6453755011
## density pH sulphates
## 0.0007448114 0.0538380692 0.0814642468
## alcohol
## 0.8921808573Volatile Acidity and Citric Acid:
They have a strong negative correlation of approximately -0.80, suggesting that as the volatile acidity increases, the citric acid tends to decrease, and vice versa. Chlorides and Density:
They exhibit a strong positive correlation of approximately 0.96, indicating that as the concentration of chlorides increases, the density also tends to increase. Free Sulfur Dioxide and Total Sulfur Dioxide:
They show a moderate positive correlation of approximately 0.91, suggesting that the levels of free sulfur dioxide and total sulfur dioxide tend to increase together. Sulphates and Chlorides:
They display a strong negative correlation of approximately -0.99, indicating an inverse relationship between sulphates and chlorides. pH and Citric Acid:
They have a moderate negative correlation of approximately -0.92, suggesting an inverse relationship between pH and citric acid. Sulphates and Density:
They exhibit a strong negative correlation of approximately -0.98, indicating an inverse relationship between sulphates and density. Alcohol and Chlorides:
They show a strong negative correlation of approximately -0.96, suggesting that as the alcohol content increases, the concentration of chlorides tends to decrease.
# Computing Correlation Matrix
corrm.emp <- cor(grouped_avg[-1])
corrm.emp## volatile acidity citric acid chlorides
## volatile acidity 1.0000000 -0.8027789 0.958746076
## citric acid -0.8027789 1.0000000 -0.906478075
## chlorides 0.9587461 -0.9064781 1.000000000
## free sulfur dioxide -0.4688300 -0.1222912 -0.227057780
## total sulfur dioxide -0.2411134 -0.2643255 0.004451211
## density 0.8807462 -0.8183699 0.959855450
## pH 0.7937278 -0.9244950 0.843412095
## sulphates -0.9299123 0.8648062 -0.985194279
## alcohol -0.8513089 0.9150260 -0.960026969
## free sulfur dioxide total sulfur dioxide density
## volatile acidity -0.4688300123 -0.241113429 0.88074617
## citric acid -0.1222911510 -0.264325499 -0.81836987
## chlorides -0.2270577802 0.004451211 0.95985545
## free sulfur dioxide 1.0000000000 0.910917763 -0.18537720
## total sulfur dioxide 0.9109177631 1.000000000 0.07769861
## density -0.1853771978 0.077698613 1.00000000
## pH -0.0204062350 0.007064232 0.70923566
## sulphates 0.2102299594 -0.069776628 -0.98264313
## alcohol -0.0002273318 -0.263807504 -0.96484059
## pH sulphates alcohol
## volatile acidity 0.793727768 -0.92991231 -0.8513088926
## citric acid -0.924495029 0.86480622 0.9150260204
## chlorides 0.843412095 -0.98519428 -0.9600269688
## free sulfur dioxide -0.020406235 0.21022996 -0.0002273318
## total sulfur dioxide 0.007064232 -0.06977663 -0.2638075044
## density 0.709235663 -0.98264313 -0.9648405891
## pH 1.000000000 -0.74690293 -0.7650657404
## sulphates -0.746902928 1.00000000 0.9774806274
## alcohol -0.765065740 0.97748063 1.0000000000plot(corrm.emp)
grouped_avg_pca <- prcomp(grouped_avg[-1], scale=TRUE)
summary(grouped_avg_pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.5173 1.4521 0.6913 0.25041 0.11870 0.00000000000004488
## Proportion of Variance 0.7041 0.2343 0.0531 0.00697 0.00157 0.00000000000000000
## Cumulative Proportion 0.7041 0.9384 0.9915 0.99843 1.00000 1.00000000000000000plot(grouped_avg_pca)
The first principal component explains the highest variance with an eigenvalue of 6.337. The second principal component explains a substantial amount of variance with an eigenvalue of 2.109. The third principal component explains a moderate amount of variance with an eigenvalue of 0.478. The fourth to sixth principal components have eigenvalues of 0.063, 0.014, and 0.000 respectively, indicating diminishing contribution to the overall variance explained.
# Load required libraries
library(cluster) # For cluster analysis
library(readr) # For reading data files
library(factoextra) # For visualizing multivariate analysis results
library(magrittr) # For using the pipe operator %>%
library(NbClust) # For determining the optimal number of clusters#for loading csv data
library(readr)
# Read the CSV file
df <- read_csv("C:/Rutgers/Subjects/Spring Sem/Multivariate Analysis/Data/wine.csv")## Rows: 1599 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): quality
## dbl (11): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#dataframe
df## # A tibble: 1,599 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr>attach(df)## The following objects are masked from df (pos = 3):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 4):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 16):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 19):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidityxtabs(~ quality + alcohol, data = df)## alcohol
## quality 8.4 8.5 8.7 8.8 9 9.05 9.1 9.2 9.23333333333333 9.25 9.3 9.4 9.5 9.55
## bad 1 1 0 2 14 1 16 53 0 0 46 81 97 1
## good 1 0 2 0 16 0 7 19 1 1 13 22 42 1
## alcohol
## quality 9.56666666666667 9.6 9.7 9.8 9.9 9.95 10 10.0333333333333 10.1 10.2
## bad 0 44 38 53 27 1 33 0 24 22
## good 1 15 16 25 22 0 34 2 23 24
## alcohol
## quality 10.3 10.4 10.5 10.55 10.6 10.7 10.75 10.8 10.9 11 11.0666666666667 11.1
## bad 14 15 32 0 8 8 0 9 17 24 0 8
## good 19 26 35 2 20 19 1 33 32 35 1 19
## alcohol
## quality 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 11.95 12 12.1 12.2 12.3 12.4
## bad 11 6 11 5 1 1 3 1 1 2 1 1 0 0
## good 25 26 21 25 14 22 26 19 0 19 12 11 12 13
## alcohol
## quality 12.5 12.6 12.7 12.8 12.9 13 13.1 13.2 13.3 13.4 13.5 13.5666666666667
## bad 0 1 0 1 2 4 1 0 0 0 0 0
## good 21 5 9 16 7 2 1 1 3 3 1 1
## alcohol
## quality 13.6 14 14.9
## bad 0 0 1
## good 4 7 0xtabs(~ quality + `citric acid`, data = df)## citric acid
## quality 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14
## bad 70 12 20 20 19 13 12 11 17 15 21 9 12 9 14
## good 62 21 30 10 10 7 12 11 16 15 14 6 15 9 7
## citric acid
## quality 0.15 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28
## bad 11 4 11 9 13 17 22 16 17 25 18 28 8 10
## good 8 5 5 13 8 8 11 11 8 26 9 10 12 9
## citric acid
## quality 0.29 0.3 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4 0.41 0.42
## bad 14 12 9 14 11 6 6 11 6 5 9 8 5 7
## good 7 18 21 18 14 18 7 9 13 9 19 21 11 22
## citric acid
## quality 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56
## bad 5 9 5 2 3 8 35 10 3 7 1 5 4 1
## good 10 14 17 17 15 15 33 10 10 10 13 8 8 7
## citric acid
## quality 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7
## bad 3 4 4 5 2 0 3 2 5 3 0 3 0 2
## good 6 5 4 4 0 1 7 7 2 11 2 8 4 0
## citric acid
## quality 0.71 0.72 0.73 0.74 0.75 0.76 0.78 0.79 1
## bad 0 0 0 1 0 1 0 1 1
## good 1 1 3 3 1 2 1 0 0xtabs(~ quality + `fixed acidity`, data = df)## fixed acidity
## quality 4.6 4.7 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4
## bad 1 0 0 2 0 1 1 0 0 10 1 1 3 6 10 11 5 11
## good 0 1 1 4 4 5 3 5 1 4 1 3 6 7 6 9 9 14
## fixed acidity
## quality 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8 8.1 8.2
## bad 8 17 13 22 17 24 34 32 25 25 28 27 28 30 17 19 19 16
## good 9 20 15 24 21 26 23 35 19 19 24 19 21 23 25 23 7 29
## fixed acidity
## quality 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 10
## bad 19 15 9 17 10 19 12 17 8 10 12 5 7 8 5 6 19 9
## good 21 11 10 10 14 15 21 9 21 6 10 12 7 9 4 9 7 14
## fixed acidity
## quality 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 11 11.1 11.2 11.3 11.4
## bad 1 6 2 5 1 5 4 5 1 2 2 2 2 1
## good 9 13 9 16 11 9 6 5 7 1 7 3 5 4
## fixed acidity
## quality 11.5 11.6 11.7 11.8 11.9 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8
## bad 2 4 1 0 3 1 1 2 4 1 4 0 3 0
## good 11 8 2 3 9 6 0 2 1 3 3 4 1 5
## fixed acidity
## quality 12.9 13 13.2 13.3 13.4 13.5 13.7 13.8 14 14.3 15 15.5 15.6 15.9
## bad 0 1 1 1 0 1 0 0 0 0 0 2 1 1
## good 2 2 2 2 1 0 2 1 1 1 2 0 1 0xtabs(~ quality + `volatile acidity`, data=df)## volatile acidity
## quality 0.12 0.16 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29
## bad 0 0 2 1 1 1 0 2 2 0 2 1 5 3
## good 3 2 8 1 2 5 6 3 11 7 14 13 18 13
## volatile acidity
## quality 0.295 0.3 0.305 0.31 0.315 0.32 0.33 0.34 0.35 0.36 0.365 0.37 0.38
## bad 0 0 0 4 0 10 4 9 7 5 2 9 11
## good 1 16 2 26 2 13 16 21 15 33 0 15 24
## volatile acidity
## quality 0.39 0.395 0.4 0.41 0.415 0.42 0.43 0.44 0.45 0.46 0.47 0.475 0.48 0.49
## bad 16 0 9 16 1 12 23 7 12 17 10 0 9 16
## good 19 2 28 17 2 19 20 16 10 14 11 2 15 19
## volatile acidity
## quality 0.5 0.51 0.52 0.53 0.54 0.545 0.55 0.56 0.565 0.57 0.575 0.58 0.585
## bad 25 2 15 8 15 0 11 21 1 12 3 18 1
## good 21 22 18 21 16 5 9 13 0 16 0 20 2
## volatile acidity
## quality 0.59 0.595 0.6 0.605 0.61 0.615 0.62 0.625 0.63 0.635 0.64 0.645 0.65
## bad 22 1 31 1 18 4 16 2 16 4 19 6 8
## good 17 0 16 2 9 2 8 1 13 5 8 6 8
## volatile acidity
## quality 0.655 0.66 0.665 0.67 0.675 0.68 0.685 0.69 0.695 0.7 0.705 0.71 0.715
## bad 7 16 3 13 3 9 7 14 6 6 5 2 7
## good 0 10 0 10 0 3 4 9 1 4 1 1 5
## volatile acidity
## quality 0.72 0.725 0.73 0.735 0.74 0.745 0.75 0.755 0.76 0.765 0.77 0.775 0.78
## bad 4 6 6 5 5 3 3 2 2 3 3 3 8
## good 1 3 0 3 6 2 3 1 3 2 3 1 2
## volatile acidity
## quality 0.785 0.79 0.795 0.8 0.805 0.81 0.815 0.82 0.825 0.83 0.835 0.84 0.845
## bad 4 1 1 2 1 1 2 5 0 3 3 2 1
## good 4 1 1 1 0 1 1 0 1 1 1 6 0
## volatile acidity
## quality 0.85 0.855 0.86 0.865 0.87 0.875 0.88 0.885 0.89 0.895 0.9 0.91 0.915
## bad 1 0 1 0 4 2 5 5 1 0 2 2 3
## good 1 3 1 1 0 0 0 0 0 1 1 1 1
## volatile acidity
## quality 0.92 0.935 0.95 0.955 0.96 0.965 0.975 0.98 1 1.005 1.01 1.02 1.025
## bad 1 2 1 1 2 3 1 2 1 1 0 3 1
## good 0 0 0 0 1 0 0 1 2 0 1 1 0
## volatile acidity
## quality 1.035 1.04 1.07 1.09 1.115 1.13 1.18 1.185 1.24 1.33 1.58
## bad 1 2 1 1 1 1 1 1 1 2 1
## good 0 1 0 0 0 0 0 0 0 0 0xtabs(~ quality + `residual sugar`, data=df)## residual sugar
## quality 0.9 1.2 1.3 1.4 1.5 1.6 1.65 1.7 1.75 1.8 1.9 2 2.05 2.1 2.15 2.2 2.25
## bad 0 2 3 15 17 38 1 33 1 60 56 77 0 52 0 59 1
## good 2 6 2 20 13 20 1 43 1 69 61 79 2 76 2 72 0
## residual sugar
## quality 2.3 2.35 2.4 2.5 2.55 2.6 2.65 2.7 2.8 2.85 2.9 2.95 3 3.1 3.2 3.3 3.4
## bad 54 1 26 37 1 42 0 18 19 1 12 0 13 3 4 6 12
## good 55 0 60 47 0 37 1 21 30 0 12 1 12 4 11 5 3
## residual sugar
## quality 3.45 3.5 3.6 3.65 3.7 3.75 3.8 3.9 4 4.1 4.2 4.25 4.3 4.4 4.5 4.6 4.65
## bad 1 1 1 0 1 1 5 6 2 2 3 1 4 2 4 1 2
## good 0 1 7 1 3 0 3 0 9 4 2 0 4 2 0 5 0
## residual sugar
## quality 4.7 4.8 5 5.1 5.15 5.2 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.55
## bad 0 2 0 1 1 0 1 4 4 1 1 2 1 4 0 1 2 0
## good 1 1 1 4 0 3 0 4 2 0 3 1 3 0 3 1 1 2
## residual sugar
## quality 6.6 6.7 7 7.2 7.3 7.5 7.8 7.9 8.1 8.3 8.6 8.8 8.9 9 10.7 11 12.9 13.4
## bad 2 1 1 1 1 1 2 3 2 0 0 0 0 0 0 0 1 0
## good 0 1 0 0 0 0 0 0 0 3 1 2 1 1 1 2 0 1
## residual sugar
## quality 13.8 13.9 15.4 15.5
## bad 2 0 0 1
## good 0 1 2 0xtabs(~ quality + chlorides, data=df)## chlorides
## quality 0.012 0.034 0.038 0.039 0.041 0.042 0.043 0.044 0.045 0.046 0.047 0.048
## bad 0 0 0 1 1 1 0 0 2 0 2 2
## good 2 1 2 3 3 2 1 5 2 4 2 6
## chlorides
## quality 0.049 0.05 0.051 0.052 0.053 0.054 0.055 0.056 0.057 0.058 0.059 0.06
## bad 5 4 0 1 4 4 3 3 6 5 3 5
## good 3 8 1 9 1 9 5 6 4 9 14 11
## chlorides
## quality 0.061 0.062 0.063 0.064 0.065 0.066 0.067 0.068 0.069 0.07 0.071 0.072
## bad 3 6 5 4 5 9 12 9 9 21 21 8
## good 8 18 17 16 18 23 15 21 12 14 26 16
## chlorides
## quality 0.073 0.074 0.075 0.076 0.077 0.078 0.079 0.08 0.081 0.082 0.083 0.084
## bad 14 23 20 29 24 23 19 39 24 23 17 24
## good 21 32 25 22 23 28 24 27 16 23 18 25
## chlorides
## quality 0.085 0.086 0.087 0.088 0.089 0.09 0.091 0.092 0.093 0.094 0.095 0.096
## bad 8 19 14 17 16 12 6 9 9 9 10 11
## good 17 12 11 15 9 9 13 13 12 10 13 7
## chlorides
## quality 0.097 0.098 0.099 0.1 0.101 0.102 0.103 0.104 0.105 0.106 0.107 0.108
## bad 9 9 8 7 3 7 3 9 5 6 5 1
## good 9 3 0 6 2 3 4 7 1 2 4 0
## chlorides
## quality 0.109 0.11 0.111 0.112 0.113 0.114 0.115 0.116 0.117 0.118 0.119 0.12
## bad 1 3 5 6 1 6 4 1 2 3 0 1
## good 2 5 2 0 0 5 1 1 2 5 3 2
## chlorides
## quality 0.121 0.122 0.123 0.124 0.125 0.126 0.127 0.128 0.132 0.136 0.137 0.143
## bad 0 5 2 2 0 1 1 0 1 1 1 0
## good 2 2 4 1 1 0 0 1 3 0 0 1
## chlorides
## quality 0.145 0.146 0.147 0.148 0.152 0.153 0.157 0.159 0.161 0.165 0.166 0.168
## bad 1 1 1 0 2 0 1 1 1 1 3 1
## good 0 0 0 1 0 1 2 0 0 0 0 0
## chlorides
## quality 0.169 0.17 0.171 0.172 0.174 0.176 0.178 0.186 0.19 0.194 0.2 0.205
## bad 1 1 0 1 0 1 1 1 0 0 1 2
## good 0 0 2 0 1 0 1 0 1 1 0 0
## chlorides
## quality 0.213 0.214 0.216 0.222 0.226 0.23 0.235 0.236 0.241 0.243 0.25 0.263
## bad 0 0 0 0 0 0 1 1 0 0 0 1
## good 1 3 1 1 2 1 0 0 1 1 1 0
## chlorides
## quality 0.267 0.27 0.332 0.337 0.341 0.343 0.358 0.36 0.368 0.369 0.387 0.401
## bad 1 1 0 0 0 1 0 1 1 1 1 1
## good 0 0 1 1 1 0 1 0 0 0 0 0
## chlorides
## quality 0.403 0.413 0.414 0.415 0.422 0.464 0.467 0.61 0.611
## bad 1 0 1 2 1 1 1 1 1
## good 0 1 1 1 0 0 0 0 0xtabs(~ quality + `free sulfur dioxide`, data=df)## free sulfur dioxide
## quality 1 2 3 4 5 5.5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
## bad 0 0 20 23 54 0 44 33 24 32 40 25 40 21 19 39 30 24 18 16 17 23 8
## good 3 1 29 18 50 1 94 38 32 30 39 34 35 36 31 39 31 36 28 23 13 18 14
## free sulfur dioxide
## quality 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 37.5 38 39 40 40.5 41 42
## bad 16 13 14 13 16 15 9 9 10 13 5 12 10 7 2 0 3 3 3 0 2 1
## good 16 21 10 19 13 8 14 7 10 9 6 6 5 4 1 2 6 2 3 1 5 2
## free sulfur dioxide
## quality 43 45 46 47 48 50 51 52 53 54 55 57 66 68 72
## bad 2 1 1 1 3 1 3 2 0 0 0 1 1 2 0
## good 1 2 0 0 1 1 1 1 1 1 2 0 0 0 1xtabs(~ quality + `total sulfur dioxide`, data=df)## total sulfur dioxide
## quality 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
## bad 2 2 5 5 4 12 16 16 14 13 9 7 13 12 17 6 14 19 5 8 13 9 13 7
## good 1 2 9 9 23 14 13 12 19 22 17 20 22 17 16 19 11 15 31 19 11 21 30 13
## total sulfur dioxide
## quality 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
## bad 4 15 12 6 7 7 5 13 13 6 10 3 12 4 6 10 4 10 9 10 3 5 8 6
## good 10 17 8 11 13 19 7 13 18 10 7 11 14 14 17 10 13 14 12 11 8 6 7 8
## total sulfur dioxide
## quality 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77
## bad 9 6 4 3 7 4 9 5 3 7 4 10 7 8 7 2 3 6 8 4 1 4 1 4
## good 11 7 6 3 7 5 9 4 6 6 6 7 2 4 3 6 5 1 2 3 7 1 2 4
## total sulfur dioxide
## quality 77.5 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 98 99 100
## bad 2 3 1 1 1 3 1 6 8 6 4 8 4 5 2 5 1 4 2 5 4 5 1
## good 0 1 4 3 5 1 1 0 1 4 2 6 5 0 5 3 1 4 2 0 3 1 2
## total sulfur dioxide
## quality 101 102 103 104 105 106 108 109 110 111 112 113 114 115 116 119 120 121
## bad 3 5 0 5 3 4 3 1 6 3 3 4 1 2 1 7 2 4
## good 1 1 2 0 2 2 0 3 0 0 0 0 1 0 0 0 0 0
## total sulfur dioxide
## quality 122 124 125 126 127 128 129 130 131 133 134 135 136 139 140 141 142 143
## bad 3 3 2 1 2 2 3 1 3 3 2 2 0 1 1 3 1 2
## good 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
## total sulfur dioxide
## quality 144 145 147 148 149 151 152 153 155 160 165 278 289
## bad 3 3 3 1 0 2 1 1 1 0 0 0 0
## good 0 0 0 1 1 0 0 0 0 1 1 1 1xtabs(~ quality + density, data=df)## density
## quality 0.99007 0.9902 0.99064 0.9908 0.99084 0.9912 0.9915 0.99154 0.99157
## bad 0 0 0 0 0 0 0 0 0
## good 2 1 2 1 1 1 1 1 1
## density
## quality 0.9916 0.99162 0.9917 0.99182 0.99191 0.9921 0.9922 0.99235 0.99236
## bad 0 0 0 0 0 0 0 0 0
## good 2 1 1 2 1 1 2 1 1
## density
## quality 0.9924 0.99242 0.99252 0.99256 0.99258 0.99264 0.9927 0.9928 0.99286
## bad 0 0 0 1 1 0 0 0 1
## good 3 2 1 0 2 1 1 2 0
## density
## quality 0.9929 0.99292 0.99294 0.99306 0.99314 0.99316 0.99318 0.9932 0.99322
## bad 0 0 0 1 0 0 0 0 1
## good 1 1 2 0 1 2 1 1 0
## density
## quality 0.99323 0.99328 0.9933 0.99331 0.99332 0.99334 0.99336 0.9934 0.99341
## bad 0 0 0 0 0 1 1 2 1
## good 1 1 1 2 1 0 0 2 0
## density
## quality 0.99344 0.99346 0.99348 0.9935 0.99352 0.99354 0.99356 0.99357 0.99358
## bad 0 1 1 0 0 0 0 0 1
## good 1 2 0 1 2 2 4 1 2
## density
## quality 0.9936 0.99362 0.99364 0.9937 0.99371 0.99374 0.99376 0.99378 0.99379
## bad 0 1 0 2 0 1 0 1 0
## good 2 1 1 0 2 1 3 2 1
## density
## quality 0.9938 0.99384 0.99385 0.99386 0.99387 0.99388 0.99392 0.99394 0.99395
## bad 1 0 1 0 0 0 1 0 1
## good 0 1 0 1 1 2 1 1 0
## density
## quality 0.99396 0.99397 0.994 0.99402 0.99408 0.9941 0.99414 0.99416 0.99417
## bad 1 0 1 2 0 1 0 0 0
## good 2 1 1 2 3 0 2 1 1
## density
## quality 0.99418 0.99419 0.9942 0.99425 0.99426 0.99428 0.9943 0.99434 0.99437
## bad 0 0 3 1 0 0 1 0 1
## good 2 2 0 0 1 1 1 1 0
## density
## quality 0.99438 0.99439 0.9944 0.99444 0.99448 0.99451 0.99454 0.99456 0.99458
## bad 0 0 1 0 3 0 0 0 0
## good 5 1 2 4 1 1 1 1 4
## density
## quality 0.99459 0.9946 0.99462 0.99464 0.99467 0.99468 0.9947 0.99471 0.99472
## bad 0 1 1 2 0 0 0 1 1
## good 1 4 1 0 2 1 6 2 2
## density
## quality 0.99473 0.99474 0.99476 0.99478 0.99479 0.9948 0.99483 0.99484 0.99486
## bad 0 0 2 1 0 1 0 0 0
## good 1 1 1 1 1 8 1 3 1
## density
## quality 0.99488 0.99489 0.9949 0.99491 0.99492 0.99494 0.99495 0.99496 0.99498
## bad 1 0 1 0 0 2 0 1 1
## good 3 3 3 1 2 2 2 0 4
## density
## quality 0.99499 0.995 0.99501 0.99502 0.99504 0.99506 0.99508 0.99509 0.9951
## bad 1 4 1 1 0 0 1 0 1
## good 0 6 0 1 2 1 2 1 3
## density
## quality 0.99512 0.99514 0.99516 0.99517 0.99518 0.99519 0.9952 0.99521 0.99522
## bad 0 0 2 1 1 0 2 1 1
## good 2 5 4 0 2 1 7 0 3
## density
## quality 0.99523 0.99524 0.99525 0.99526 0.99528 0.99529 0.9953 0.99531 0.99532
## bad 0 2 1 0 0 1 1 0 1
## good 1 2 1 2 3 0 3 2 0
## density
## quality 0.99533 0.99534 0.99536 0.99538 0.9954 0.99541 0.99542 0.99543 0.99544
## bad 0 1 0 3 3 0 1 0 0
## good 1 5 2 8 1 1 0 2 1
## density
## quality 0.99545 0.99546 0.99547 0.99549 0.9955 0.99551 0.99552 0.99553 0.99554
## bad 1 5 1 0 6 2 1 0 0
## good 2 2 1 2 8 1 4 1 3
## density
## quality 0.99555 0.99556 0.99557 0.99558 0.9956 0.99562 0.99564 0.99565 0.99566
## bad 0 0 1 2 6 2 0 1 1
## good 1 2 2 1 8 2 2 2 3
## density
## quality 0.99568 0.99569 0.9957 0.99572 0.99573 0.99574 0.99575 0.99576 0.99577
## bad 1 1 5 3 0 0 2 4 1
## good 3 0 1 6 1 2 0 1 2
## density
## quality 0.99578 0.9958 0.99581 0.99582 0.99584 0.99585 0.99586 0.99587 0.99588
## bad 2 9 0 0 2 1 2 2 1
## good 1 5 1 1 0 2 4 0 3
## density
## quality 0.99589 0.9959 0.99592 0.99593 0.99594 0.99596 0.99598 0.99599 0.996
## bad 0 6 3 1 0 2 0 0 7
## good 1 7 1 1 1 0 2 2 6
## density
## quality 0.99603 0.99604 0.99605 0.99606 0.99608 0.99609 0.9961 0.99612 0.99613
## bad 2 1 1 2 1 0 8 4 2
## good 0 2 2 0 1 1 2 2 2
## density
## quality 0.99614 0.99615 0.99616 0.99617 0.99619 0.9962 0.99621 0.99622 0.99623
## bad 1 0 3 0 0 16 1 3 2
## good 1 5 4 1 1 12 0 2 0
## density
## quality 0.99624 0.99625 0.99627 0.99628 0.99629 0.9963 0.99631 0.99632 0.99633
## bad 2 2 1 2 2 10 1 4 2
## good 1 1 2 1 0 5 0 0 2
## density
## quality 0.99634 0.99635 0.99636 0.99638 0.99639 0.9964 0.99641 0.99642 0.99643
## bad 3 0 4 1 1 14 1 1 0
## good 0 1 1 4 1 11 0 2 1
## density
## quality 0.99645 0.99646 0.99647 0.99648 0.99649 0.9965 0.99651 0.99652 0.99654
## bad 1 0 2 2 0 6 0 1 2
## good 0 1 0 1 1 5 1 5 0
## density
## quality 0.99655 0.99656 0.99658 0.99659 0.9966 0.99661 0.99664 0.99665 0.99666
## bad 4 2 0 0 18 0 2 1 2
## good 2 3 1 2 5 1 1 0 1
## density
## quality 0.99667 0.99668 0.99669 0.9967 0.99672 0.99674 0.99675 0.99676 0.99677
## bad 0 3 2 6 2 0 1 0 0
## good 1 1 0 7 3 2 4 3 2
## density
## quality 0.99678 0.9968 0.99682 0.99683 0.99684 0.99685 0.99686 0.99688 0.99689
## bad 0 23 1 1 0 3 0 0 4
## good 1 12 1 1 1 5 3 2 0
## density
## quality 0.9969 0.99692 0.99693 0.99694 0.99695 0.99697 0.99698 0.99699 0.997
## bad 14 2 1 3 0 0 0 1 15
## good 4 2 1 0 1 1 1 0 9
## density
## quality 0.99701 0.99702 0.99704 0.99705 0.99706 0.99708 0.99709 0.9971 0.99712
## bad 1 2 0 1 2 2 1 8 4
## good 1 2 3 0 0 2 0 5 0
## density
## quality 0.99713 0.99714 0.99716 0.99717 0.99718 0.99719 0.9972 0.99721 0.99722
## bad 0 1 0 1 3 1 13 0 1
## good 2 1 2 0 0 0 23 1 0
## density
## quality 0.99724 0.99725 0.99726 0.99727 0.99728 0.99729 0.9973 0.99732 0.99733
## bad 1 0 1 0 1 1 9 2 1
## good 3 1 0 1 2 0 9 1 0
## density
## quality 0.99734 0.99735 0.99736 0.99738 0.99739 0.9974 0.99743 0.99744 0.99745
## bad 1 4 2 3 0 11 2 0 5
## good 3 2 3 1 1 11 0 2 4
## density
## quality 0.99746 0.99747 0.99748 0.9975 0.99752 0.99754 0.99756 0.99758 0.9976
## bad 7 0 3 4 1 0 1 0 21
## good 0 2 0 3 0 1 0 1 14
## density
## quality 0.99761 0.99764 0.99765 0.99768 0.99769 0.9977 0.99772 0.99774 0.99779
## bad 0 0 1 1 1 1 1 0 1
## good 1 1 0 2 1 3 0 5 0
## density
## quality 0.9978 0.99782 0.99783 0.99784 0.99785 0.99786 0.99787 0.99788 0.9979
## bad 21 2 2 1 1 3 1 2 9
## good 5 0 0 0 0 1 2 0 5
## density
## quality 0.99791 0.99796 0.99798 0.998 0.99801 0.99803 0.99808 0.9981 0.99814
## bad 1 1 1 16 2 3 1 5 1
## good 0 0 1 13 0 0 0 5 1
## density
## quality 0.99815 0.99817 0.99818 0.9982 0.99822 0.99823 0.99824 0.99828 0.9983
## bad 0 2 3 12 0 1 1 2 5
## good 2 0 0 11 1 0 2 0 4
## density
## quality 0.99832 0.99834 0.99836 0.9984 0.99842 0.99845 0.9985 0.99852 0.99854
## bad 1 0 1 7 0 1 1 1 1
## good 0 1 1 13 2 0 2 0 0
## density
## quality 0.99855 0.99859 0.9986 0.99864 0.99865 0.9987 0.99878 0.9988 0.99888
## bad 2 0 10 0 0 4 1 9 0
## good 0 1 9 1 2 8 0 11 2
## density
## quality 0.9989 0.99892 0.999 0.99901 0.9991 0.99914 0.99915 0.99918 0.9992
## bad 0 3 2 0 4 3 1 1 4
## good 2 0 6 1 6 0 0 0 3
## density
## quality 0.99922 0.99925 0.9993 0.99935 0.99938 0.99939 0.9994 0.9995 0.9996
## bad 1 0 1 1 1 0 9 0 5
## good 0 1 3 0 0 1 15 1 7
## density
## quality 0.99965 0.9997 0.99974 0.99975 0.99976 0.9998 0.9999 1 1.00005 1.0001
## bad 0 5 0 1 1 6 1 3 0 4
## good 1 3 1 0 0 4 0 7 2 0
## density
## quality 1.00012 1.00015 1.0002 1.00024 1.00025 1.0003 1.0004 1.0006 1.0008
## bad 0 2 2 1 1 0 7 3 1
## good 1 0 8 0 0 2 2 3 2
## density
## quality 1.001 1.0014 1.0015 1.0018 1.0021 1.0022 1.00242 1.0026 1.00289 1.00315
## bad 4 1 0 1 0 0 2 1 1 3
## good 2 5 2 0 2 2 0 1 0 0
## density
## quality 1.0032 1.00369
## bad 0 0
## good 1 2xtabs(~ quality + pH, data=df)## pH
## quality 2.74 2.86 2.87 2.88 2.89 2.9 2.92 2.93 2.94 2.95 2.98 2.99 3 3.01 3.02
## bad 1 0 0 1 2 0 3 1 2 0 2 2 4 1 3
## good 0 1 1 1 2 1 1 2 2 1 3 0 2 4 5
## pH
## quality 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.1 3.11 3.12 3.13 3.14 3.15 3.16
## bad 4 5 5 3 1 7 5 10 3 6 10 12 17 21
## good 2 5 3 7 10 4 6 9 6 14 3 9 17 15
## pH
## quality 3.17 3.18 3.19 3.2 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 3.3
## bad 12 11 15 20 11 13 13 14 17 26 12 25 24 26
## good 15 19 10 19 25 26 19 15 9 27 23 17 22 31
## pH
## quality 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39 3.4 3.41 3.42 3.43 3.44
## bad 16 14 13 23 16 32 15 19 26 15 15 10 7 15
## good 23 31 24 20 23 24 22 29 22 22 19 23 10 14
## pH
## quality 3.45 3.46 3.47 3.48 3.49 3.5 3.51 3.52 3.53 3.54 3.55 3.56 3.57 3.58
## bad 9 15 4 13 7 8 9 7 10 8 4 4 5 4
## good 11 7 17 6 3 6 6 11 7 8 4 7 5 6
## pH
## quality 3.59 3.6 3.61 3.62 3.63 3.66 3.67 3.68 3.69 3.7 3.71 3.72 3.74 3.75
## bad 5 3 3 3 3 3 2 2 2 0 0 2 1 1
## good 3 4 5 1 0 1 1 3 2 1 4 1 0 0
## pH
## quality 3.78 3.85 3.9 4.01
## bad 0 0 1 0
## good 2 1 1 2xtabs(~ quality + sulphates, data=df)## sulphates
## quality 0.33 0.37 0.39 0.4 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51
## bad 1 2 4 2 4 6 13 12 14 15 21 23 21 18
## good 0 0 2 2 1 2 3 0 4 4 8 8 6 8
## sulphates
## quality 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65
## bad 36 34 41 30 30 36 38 21 24 27 31 21 22 12
## good 11 17 27 20 30 19 30 30 45 18 30 27 24 29
## sulphates
## quality 0.66 0.67 0.68 0.69 0.7 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79
## bad 15 16 13 5 8 11 8 8 8 4 1 5 9 5
## good 27 20 22 18 25 15 20 18 18 16 24 21 14 13
## sulphates
## quality 0.8 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 0.92 0.93
## bad 7 1 3 3 2 0 3 2 2 0 1 3 0 4
## good 12 14 19 12 11 14 10 11 5 7 7 5 5 6
## sulphates
## quality 0.94 0.95 0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07
## bad 1 1 0 2 1 1 0 0 1 1 0 2 1 2
## good 3 1 3 4 1 2 1 1 2 1 2 1 3 0
## sulphates
## quality 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.2 1.22 1.26
## bad 1 1 1 0 1 0 2 1 0 5 1 1 1 1
## good 2 0 1 1 0 2 0 0 1 0 2 0 0 0
## sulphates
## quality 1.28 1.31 1.33 1.34 1.36 1.56 1.59 1.61 1.62 1.95 1.98 2
## bad 2 1 0 1 0 1 1 0 1 0 1 1
## good 0 0 1 0 3 0 0 1 0 2 0 0# Assuming 'quality' is a column in the data frame 'df'
distinct_values <- unique(df$quality)
# Print distinct values
print(distinct_values)## [1] "bad" "good"# Count the number of unique values
num_unique <- length(distinct_values)
# Print the number of unique values
print(num_unique)## [1] 2table(df$quality)##
## bad good
## 744 855# Recode the quality variable to binary (0 for "bad" and 1 for "good")
#df$quality <- as.numeric(df$quality == "good")
df## # A tibble: 1,599 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr>- Model Insights (10 points): (Regression)
Alcohol:
Estimate: 1.00468
This indicates that for every one-unit increase in alcohol, the log-odds of the wine being of good quality (as opposed to bad quality) increase by 1.00468.
Std. Error: 0.06884 This shows the standard error associated with the coefficient estimate.
z value: 14.594
The z value is the coefficient estimate divided by its standard error. It indicates the number of standard deviations a parameter estimate is from the null hypothesis value of 0. The higher the z value, the more significant the predictor.
p-value: <0.0000000000000002 ***
The p-value indicates the significance of each predictor. In this case, the p-value is extremely low, indicating a significant relationship between alcohol and the quality of the wine.
Volatile Acidity:
Estimate: -3.54124
This indicates that for every one-unit increase in volatile acidity, the log-odds of the wine being of good quality (as opposed to bad quality) decrease by 3.54124.
Std. Error: 0.35470 This shows the standard error associated with the coefficient estimate.
z value: -9.984
The z value is the coefficient estimate divided by its standard error. It indicates the number of standard deviations a parameter estimate is from the null hypothesis value of 0. The more negative the z value, the more significant the negative relationship.
p-value: <0.0000000000000002 ***
The p-value indicates the significance of each predictor. In this case, the p-value is extremely low, indicating a significant relationship between volatile acidity and the quality of the wine.
attach(df)## The following objects are masked from df (pos = 3):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 4):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 5):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 17):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity## The following objects are masked from df (pos = 20):
##
## alcohol, chlorides, citric acid, density, fixed acidity, free
## sulfur dioxide, pH, quality, residual sugar, sulphates, total
## sulfur dioxide, volatile acidity#df <- df[, !names(df) %in% "quality_new"]
# Fit logistic regression model with the binary indicator
# Fit logistic regression model with binary indicator
# Convert 'quality' variable to a factor
df$quality <- factor(df$quality, levels = c("bad", "good"))
logistic_simple <- glm(quality ~ alcohol + `volatile acidity` , data = df, family = "binomial")
# Summary of the model
summary(logistic_simple)##
## Call:
## glm(formula = quality ~ alcohol + `volatile acidity`, family = "binomial",
## data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.34395 0.72630 -11.488 <0.0000000000000002 ***
## alcohol 1.00468 0.06884 14.594 <0.0000000000000002 ***
## `volatile acidity` -3.54124 0.35470 -9.984 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2209.0 on 1598 degrees of freedom
## Residual deviance: 1752.5 on 1596 degrees of freedom
## AIC: 1758.5
##
## Number of Fisher Scoring iterations: 4df## # A tibble: 1,599 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <fct>predicted.data <- data.frame(probability.of.quality=logistic_simple$fitted.values,quality=df$quality)
options(scipen = 999)
predicted.data## probability.of.quality quality
## 1 0.20120458 bad
## 2 0.16598807 bad
## 3 0.23336924 bad
## 4 0.62490271 good
## 5 0.20120458 bad
## 6 0.22493539 bad
## 7 0.26412089 bad
## 8 0.35459301 good
## 9 0.29872728 good
## 10 0.60697565 bad
## 11 0.23961872 bad
## 12 0.60697565 bad
## 13 0.35998355 bad
## 14 0.20400044 bad
## 15 0.21476806 bad
## 16 0.21476806 bad
## 17 0.77095108 good
## 18 0.27220225 bad
## 19 0.19922899 bad
## 20 0.44175332 good
## 21 0.57957067 good
## 22 0.50510134 bad
## 23 0.42014450 bad
## 24 0.34634985 bad
## 25 0.49624849 good
## 26 0.40577300 bad
## 27 0.43748990 bad
## 28 0.42014450 bad
## 29 0.19557323 bad
## 30 0.31385862 good
## 31 0.35732969 bad
## 32 0.47002514 good
## 33 0.30628325 bad
## 34 0.26069390 good
## 35 0.44175332 bad
## 36 0.27228150 good
## 37 0.59433094 good
## 38 0.51395098 good
## 39 0.07588334 bad
## 40 0.64832370 bad
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## 42 0.23856923 bad
## 43 0.61539074 good
## 44 0.41753089 bad
## 45 0.23647973 bad
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## 1358 0.78335299 good
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## 1368 0.57272250 good
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## 1378 0.89825572 good
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## 1439 0.47875504 bad
## 1440 0.59915072 good
## 1441 0.84536815 good
## 1442 0.18059219 good
## 1443 0.32540927 bad
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## 1445 0.60716649 good
## 1446 0.18560102 good
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## 1449 0.34244237 bad
## 1450 0.84068220 good
## 1451 0.84536815 good
## 1452 0.82840737 good
## 1453 0.77841792 good
## 1454 0.26172995 bad
## 1455 0.73371481 good
## 1456 0.35893163 good
## 1457 0.58496807 good
## 1458 0.26172995 bad
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## 1460 0.94802144 good
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## 1467 0.50077497 good
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## 1529 0.68490444 good
## 1530 0.36827525 good
## 1531 0.81012745 good
## 1532 0.35600603 bad
## 1533 0.43181977 good
## 1534 0.33562044 bad
## 1535 0.80655262 good
## 1536 0.36674837 good
## 1537 0.53181775 good
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## 1541 0.81289694 good
## 1542 0.84836054 good
## 1543 0.36092512 good
## 1544 0.63335174 good
## 1545 0.83177460 good
## 1546 0.39469544 good
## 1547 0.47136271 bad
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## 1549 0.66558251 bad
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## 1556 0.56767638 good
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## 1559 0.26300120 bad
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## 1561 0.37231061 bad
## 1562 0.37231061 bad
## 1563 0.34123450 bad
## 1564 0.34123450 bad
## 1565 0.34123450 bad
## 1566 0.55835298 good
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## 1568 0.34123450 bad
## 1569 0.25275995 bad
## 1570 0.80280024 good
## 1571 0.94476241 good
## 1572 0.81189580 good
## 1573 0.22393127 bad
## 1574 0.89666313 good
## 1575 0.75165382 good
## 1576 0.84156002 good
## 1577 0.80911498 good
## 1578 0.75637870 good
## 1579 0.65392880 good
## 1580 0.73611832 bad
## 1581 0.91469520 good
## 1582 0.73611832 bad
## 1583 0.74645777 bad
## 1584 0.46828958 bad
## 1585 0.89818258 good
## 1586 0.86162314 good
## 1587 0.88201668 good
## 1588 0.60991492 good
## 1589 0.89832882 good
## 1590 0.15865759 bad
## 1591 0.79619940 good
## 1592 0.66593857 good
## 1593 0.71126725 good
## 1594 0.26992260 good
## 1595 0.52011249 bad
## 1596 0.72328876 good
## 1597 0.71126725 good
## 1598 0.40606239 bad
## 1599 0.83338281 good- Model Insights (10 points): (Regression)
Model Acceptance
Inference:
Volatile Acidity: As the volatile acidity level increases, the probability of wine quality being bad increases and the probability of wine quality being good decreases.
Interpretation:
At a volatile acidity level of 0.12:
Probability of Quality Being Bad: 0.0395216662402474
Probability of Quality Being Good: 0
Interpretation: At a volatile acidity level of 0.12, the probability of wine quality being bad is approximately 0.0395, and the probability of wine quality being good is 0.
At a volatile acidity level of 0.16:
Probability of Quality Being Bad: 0.0395216662402474
Probability of Quality Being Good: 0
Interpretation: At a volatile acidity level of 0.16, the probability of wine quality being bad is approximately 0.0395, and the probability of wine quality being good is 0.
At a volatile acidity level of 0.18:
Probability of Quality Being Bad: 0.0395216662402474
Probability of Quality Being Good: 0 Interpretation: At a volatile acidity level of 0.18, the probability of wine quality being bad is approximately 0.0395, and the probability of wine quality being good is 0.
At a volatile acidity level of 0.19:
Probability of Quality Being Bad: 0.0395216662402474
Probability of Quality Being Good: 0
Interpretation: At a volatile acidity level of 0.19, the probability of wine quality being bad is approximately 0.0395, and the probability of wine quality being good is 0.
This pattern continues for each level of volatile acidity, with the probability of wine quality being bad increasing and the probability of wine quality being good decreasing
xtabs(~ probability.of.quality + `volatile acidity` + `citric acid`, data=predicted.data)## , , citric acid = 0
##
## volatile acidity
## probability.of.quality 0.12 0.16 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25 0.26
## 0.0395216662402474 0 0 0 0 0 0 0 0 0 0 0
## 0.0434836018064436 0 0 0 0 0 0 0 0 0 0 0
## 0.0479688679143368 0 0 0 0 0 0 0 0 0 0 0
## 0.055042859405055 0 0 0 0 0 0 0 0 0 0 0
## 0.0758833434280786 0 0 0 0 0 0 0 0 0 0 0
## 0.0822689800017222 0 0 0 0 0 0 0 0 0 0 0
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##
## [ reached getOption("max.print") -- omitted 318 row(s) and 79 matrix slice(s) ]- Residual Analysis (2 points)
Null Deviance (2209.0):
Represents the deviance when the model only consists of the intercept.
The higher the value, the worse the model fits the data without any predictors.
Residual Deviance (1804.7):
Represents the deviance when the predictors are included in the model.
The lower the value, the better the model fits the data with predictors.
Degrees of Freedom (1598 for Null, 1596 for Residuals):
Degrees of freedom are a measure of the amount of information that went into estimation of the parameters.
It’s the number of independent values or quantities that can be assigned to a statistical distribution.
Difference (404.3):
A measure of how much better the model with predictors explains the data compared to a model with just the intercept.
A larger difference implies a better fit of the model.
logistic <- glm(quality ~ alcohol + sulphates, data=df, family="binomial")
summary(logistic)##
## Call:
## glm(formula = quality ~ alcohol + sulphates, family = "binomial",
## data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -12.32438 0.73149 -16.848 < 0.0000000000000002 ***
## alcohol 1.03724 0.06728 15.417 < 0.0000000000000002 ***
## sulphates 2.67209 0.37097 7.203 0.000000000000589 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2209.0 on 1598 degrees of freedom
## Residual deviance: 1804.7 on 1596 degrees of freedom
## AIC: 1810.7
##
## Number of Fisher Scoring iterations: 4## Now calculate the overall "Pseudo R-squared" and its p-value
ll.null <- logistic$null.deviance/-2
ll.proposed <- logistic$deviance/-2
(ll.null - ll.proposed) / ll.null## [1] 0.1830352Predicted value = bad
## The p-value for the R^2
1 - pchisq(2*(ll.proposed - ll.null), df=(length(logistic$coefficients)-1))## [1] 0predicted.data$quality <-
data.frame(probability.of.quality=logistic$fitted.values,quality=df$quality)- Model Insights (10 points): (Regression Prediction)
Prediction
Prediction seem to be accurate below
Inference:
Low Predicted Probability of Bad Quality:
Values like 0.2539028, 0.4152374, 0.3959143, 0.2539028, 0.2539028, 0.2066703, and 0.2077082 represent a low likelihood of bad quality.
For instance, a value of 0.2539028 indicates a relatively low probability of being bad quality, but the possibility is still there.
A value of 0.2066703 indicates a very low probability of bad quality.
A value of 0.2077082 indicates a very low probability of bad quality.
A value of 0.1606866 indicates an extremely low probability of bad quality.
High Predicted Probability of Bad Quality:
Values like 0.6691553, 0.6691553, 0.3393559, 0.7829626, 0.3940586, 0.4263695, 0.6774938 represent a high likelihood of bad quality.
For instance, a value of 0.6691553 indicates a high probability of being bad quality.
A value of 0.7829626 indicates a very high probability of bad quality.
Good Quality:
Values like 0.3521585, 0.3326894, 0.2793961, 0.6389391, and 0.5260103 indicate a prediction of good quality.
For instance, a value of 0.3521585 indicates a probability of being good quality, but there is still a possibility of it being bad quality.
probability:
probability.of.quality: The predicted probabilities of each quality category (bad or good).
quality: bad: Indicates low quality. good: Indicates high quality.
data.frame(probability.of.quality=logistic$fitted.values,quality=df$quality)## probability.of.quality quality
## 1 0.2539028 bad
## 2 0.4152374 bad
## 3 0.3959143 bad
## 4 0.3521585 good
## 5 0.2539028 bad
## 6 0.2539028 bad
## 7 0.2066703 bad
## 8 0.3326894 good
## 9 0.2793961 good
## 10 0.6691553 bad
## 11 0.2077082 bad
## 12 0.6691553 bad
## 13 0.3393559 bad
## 14 0.7829626 bad
## 15 0.3940586 bad
## 16 0.4263695 bad
## 17 0.6389391 good
## 18 0.6774938 bad
## 19 0.1606866 bad
## 20 0.5260103 good
## 21 0.2390186 good
## 22 0.3713936 bad
## 23 0.4902644 bad
## 24 0.2390186 bad
## 25 0.3590052 good
## 26 0.2347594 bad
## 27 0.2902815 bad
## 28 0.4902644 bad
## 29 0.2488742 bad
## 30 0.3582785 good
## 31 0.4000456 bad
## 32 0.5482271 good
## 33 0.4023225 bad
## 34 0.2341923 good
## 35 0.2121399 bad
## 36 0.2896311 good
## 37 0.6180127 good
## 38 0.4225107 good
## 39 0.2938467 bad
## 40 0.6866556 bad
## 41 0.6866556 bad
## 42 0.2116124 bad
## 43 0.7254336 good
## 44 0.8271777 bad
## 45 0.2533047 bad
## 46 0.9404701 bad
## 47 0.3034130 bad
## 48 0.2848075 bad
## 49 0.2166403 bad
## 50 0.2258470 bad
## 51 0.3090901 bad
## 52 0.2909328 good
## 53 0.2964757 good
## 54 0.2539028 bad
## 55 0.4847392 good
## 56 0.2583920 bad
## 57 0.3355008 bad
## 58 0.3736090 bad
## 59 0.4072381 bad
## 60 0.2439127 good
## 61 0.2551016 bad
## 62 0.3141443 bad
## 63 0.3070697 good
## 64 0.3460866 bad
## 65 0.5059264 bad
## 66 0.5059264 bad
## 67 0.3063979 bad
## 68 0.5802879 bad
## 69 0.7078769 bad
## 70 0.7512296 good
## 71 0.2635454 good
## 72 0.2336262 bad
## 73 0.2336262 bad
## 74 0.2033452 bad
## 75 0.4561848 bad
## 76 0.5818260 bad
## 77 0.5818260 bad
## 78 0.5606481 good
## 79 0.3872902 bad
## 80 0.5267979 bad
## 81 0.2166403 bad
## 82 0.6997279 bad
## 83 0.2038575 bad
## 84 0.6158356 bad
## 85 0.6090870 good
## 86 0.4324970 bad
## 87 0.9591024 good
## 88 0.3414840 bad
## 89 0.6877028 bad
## 90 0.1717941 bad
## 91 0.2635454 bad
## 92 0.9591024 good
## 93 0.9581682 bad
## 94 0.3414840 bad
## 95 0.4623954 bad
## 96 0.9345841 good
## 97 0.5672190 bad
## 98 0.2500573 bad
## 99 0.3400645 bad
## 100 0.1958180 good
## 101 0.4714052 good
## 102 0.4898364 good
## 103 0.1958180 good
## 104 0.1948250 bad
## 105 0.2155699 bad
## 106 0.1948250 bad
## 107 0.6947542 bad
## 108 0.3619184 bad
## 109 0.4429611 good
## 110 0.3027457 bad
## 111 0.5036235 bad
## 112 0.4023225 bad
## 113 0.4023225 bad
## 114 0.3839535 good
## 115 0.5036235 bad
## 116 0.5622038 good
## 117 0.4201998 good
## 118 0.2247442 good
## 119 0.6421817 good
## 120 0.2077082 good
## 121 0.1875389 bad
## 122 0.6421817 good
## 123 0.1990508 bad
## 124 0.2583920 bad
## 125 0.2336262 bad
## 126 0.3309677 bad
## 127 0.5722203 bad
## 128 0.5656672 bad
## 129 0.7359489 good
## 130 0.2589979 bad
## 131 0.3077424 bad
## 132 0.9449673 bad
## 133 0.9449673 bad
## 134 0.3521585 good
## 135 0.3575524 good
## 136 0.4561848 bad
## 137 0.3981833 bad
## 138 0.2551016 bad
## 139 0.2635454 bad
## 140 0.2635454 bad
## 141 0.4561848 bad
## 142 0.3981833 bad
## 143 0.9867184 good
## 144 0.2641590 bad
## 145 0.9867184 good
## 146 0.2854515 bad
## 147 0.3629827 bad
## 148 0.3681116 bad
## 149 0.4514864 good
## 150 0.5490094 good
## 151 0.4440979 good
## 152 0.9410280 bad
## 153 0.4000456 bad
## 154 0.4000456 bad
## 155 0.6202476 bad
## 156 0.6139339 bad
## 157 0.6202476 bad
## 158 0.6139339 bad
## 159 0.2071888 bad
## 160 0.2395937 good
## 161 0.2951595 bad
## 162 0.5260103 bad
## 163 0.3880402 good
## 164 0.2201228 bad
## 165 0.2201228 bad
## 166 0.3070697 bad
## 167 0.4121723 bad
## 168 0.1536091 bad
## 169 0.4831612 good
## 170 0.8554591 bad
## 171 0.1188192 bad
## 172 0.2401698 good
## 173 0.2401698 good
## 174 0.7552904 good
## 175 0.2288761 bad
## 176 0.4046036 bad
## 177 0.2288761 bad
## 178 0.5490094 good
## 179 0.2951595 bad
## 180 0.2902815 bad
## 181 0.2902815 bad
## 182 0.5118812 bad
## 183 0.2161046 bad
## 184 0.2444958 bad
## 185 0.2444958 good
## 186 0.3000848 bad
## 187 0.2121399 bad
## 188 0.2781257 bad
## 189 0.2687643 bad
## 190 0.2687643 bad
## 191 0.2854515 bad
## 192 0.3769108 good
## 193 0.3014136 bad
## 194 0.3114279 bad
## 195 0.3114279 bad
## 196 0.2439127 bad
## 197 0.4581119 bad
## 198 0.6778099 good
## 199 0.9686620 good
## 200 0.6568330 bad
## 201 0.6342191 good
## 202 0.5185552 bad
## 203 0.4110567 bad
## 204 0.2121399 bad
## 205 0.2166403 good
## 206 0.7181650 good
## 207 0.7181650 good
## 208 0.2206656 bad
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## 210 0.7146618 good
## 211 0.9445621 good
## 212 0.3880402 good
## 213 0.5114534 good
## 214 0.3319884 bad
## 215 0.4348248 good
## 216 0.6450803 bad
## 217 0.6475775 bad
## 218 0.1953210 bad
## 219 0.3057269 bad
## 220 0.2958172 bad
## 221 0.2693856 good
## 222 0.2589979 bad
## 223 0.2958172 bad
## 224 0.4137040 good
## 225 0.4965153 bad
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## 1561 0.3393559 bad
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## 1565 0.4000456 bad
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## 1568 0.4000456 bad
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## 1572 0.7162427 good
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## 1575 0.4623954 good
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## 1597 0.7482658 good
## 1598 0.5381023 bad
## 1599 0.7003355 goodggplot(data = predicted.data, aes(x = probability.of.quality, y = df$quality)) +
geom_point(aes(color = df$quality), alpha = 1, shape = 4, stroke = 2) +
xlab("Predicted probability of having good and bad quality wine") +
ylab("Binary Quality")
# From Caret
pdata <- predict(logistic,newdata=df,type="response" )
pdata## 1 2 3 4 5 6 7 8
## 0.2539028 0.4152374 0.3959143 0.3521585 0.2539028 0.2539028 0.2066703 0.3326894
## 9 10 11 12 13 14 15 16
## 0.2793961 0.6691553 0.2077082 0.6691553 0.3393559 0.7829626 0.3940586 0.4263695
## 17 18 19 20 21 22 23 24
## 0.6389391 0.6774938 0.1606866 0.5260103 0.2390186 0.3713936 0.4902644 0.2390186
## 25 26 27 28 29 30 31 32
## 0.3590052 0.2347594 0.2902815 0.4902644 0.2488742 0.3582785 0.4000456 0.5482271
## 33 34 35 36 37 38 39 40
## 0.4023225 0.2341923 0.2121399 0.2896311 0.6180127 0.4225107 0.2938467 0.6866556
## 41 42 43 44 45 46 47 48
## 0.6866556 0.2116124 0.7254336 0.8271777 0.2533047 0.9404701 0.3034130 0.2848075
## 49 50 51 52 53 54 55 56
## 0.2166403 0.2258470 0.3090901 0.2909328 0.2964757 0.2539028 0.4847392 0.2583920
## 57 58 59 60 61 62 63 64
## 0.3355008 0.3736090 0.4072381 0.2439127 0.2551016 0.3141443 0.3070697 0.3460866
## 65 66 67 68 69 70 71 72
## 0.5059264 0.5059264 0.3063979 0.5802879 0.7078769 0.7512296 0.2635454 0.2336262
## 73 74 75 76 77 78 79 80
## 0.2336262 0.2033452 0.4561848 0.5818260 0.5818260 0.5606481 0.3872902 0.5267979
## 81 82 83 84 85 86 87 88
## 0.2166403 0.6997279 0.2038575 0.6158356 0.6090870 0.4324970 0.9591024 0.3414840
## 89 90 91 92 93 94 95 96
## 0.6877028 0.1717941 0.2635454 0.9591024 0.9581682 0.3414840 0.4623954 0.9345841
## 97 98 99 100 101 102 103 104
## 0.5672190 0.2500573 0.3400645 0.1958180 0.4714052 0.4898364 0.1958180 0.1948250
## 105 106 107 108 109 110 111 112
## 0.2155699 0.1948250 0.6947542 0.3619184 0.4429611 0.3027457 0.5036235 0.4023225
## 113 114 115 116 117 118 119 120
## 0.4023225 0.3839535 0.5036235 0.5622038 0.4201998 0.2247442 0.6421817 0.2077082
## 121 122 123 124 125 126 127 128
## 0.1875389 0.6421817 0.1990508 0.2583920 0.2336262 0.3309677 0.5722203 0.5656672
## 129 130 131 132 133 134 135 136
## 0.7359489 0.2589979 0.3077424 0.9449673 0.9449673 0.3521585 0.3575524 0.4561848
## 137 138 139 140 141 142 143 144
## 0.3981833 0.2551016 0.2635454 0.2635454 0.4561848 0.3981833 0.9867184 0.2641590
## 145 146 147 148 149 150 151 152
## 0.9867184 0.2854515 0.3629827 0.3681116 0.4514864 0.5490094 0.4440979 0.9410280
## 153 154 155 156 157 158 159 160
## 0.4000456 0.4000456 0.6202476 0.6139339 0.6202476 0.6139339 0.2071888 0.2395937
## 161 162 163 164 165 166 167 168
## 0.2951595 0.5260103 0.3880402 0.2201228 0.2201228 0.3070697 0.4121723 0.1536091
## 169 170 171 172 173 174 175 176
## 0.4831612 0.8554591 0.1188192 0.2401698 0.2401698 0.7552904 0.2288761 0.4046036
## 177 178 179 180 181 182 183 184
## 0.2288761 0.5490094 0.2951595 0.2902815 0.2902815 0.5118812 0.2161046 0.2444958
## 185 186 187 188 189 190 191 192
## 0.2444958 0.3000848 0.2121399 0.2781257 0.2687643 0.2687643 0.2854515 0.3769108
## 193 194 195 196 197 198 199 200
## 0.3014136 0.3114279 0.3114279 0.2439127 0.4581119 0.6778099 0.9686620 0.6568330
## 201 202 203 204 205 206 207 208
## 0.6342191 0.5185552 0.4110567 0.2121399 0.2166403 0.7181650 0.7181650 0.2206656
## 209 210 211 212 213 214 215 216
## 0.3134640 0.7146618 0.9445621 0.3880402 0.5114534 0.3319884 0.4348248 0.6450803
## 217 218 219 220 221 222 223 224
## 0.6475775 0.1953210 0.3057269 0.2958172 0.2693856 0.2589979 0.2958172 0.4137040
## 225 226 227 228 229 230 231 232
## 0.4965153 0.5290937 0.8619425 0.3832065 0.5290937 0.6038089 0.8793942 0.3393559
## 233 234 235 236 237 238 239 240
## 0.3783959 0.6038089 0.1795317 0.1916444 0.1916444 0.2258470 0.1916444 0.1795317
## 241 242 243 244 245 246 247 248
## 0.5583777 0.7065686 0.2994217 0.3688468 0.3688468 0.6247022 0.2533047 0.1860991
## 249 250 251 252 253 254 255 256
## 0.2938467 0.6247022 0.5330275 0.4538346 0.4957255 0.2902815 0.4538346 0.2395937
## 257 258 259 260 261 262 263 264
## 0.2450798 0.2706311 0.6883809 0.6691553 0.2806700 0.2746771 0.3944041 0.2915849
## 265 266 267 268 269 270 271 272
## 0.7868953 0.5447366 0.4414026 0.9626859 0.4643924 0.6778099 0.6374801 0.6778099
## 273 274 275 276 277 278 279 280
## 0.5587344 0.2964757 0.3736090 0.6374801 0.4643924 0.6778099 0.9495987 0.6511741
## 281 282 283 284 285 286 287 288
## 0.3806277 0.6963986 0.2038575 0.6511741 0.4348248 0.4348248 0.5962910 0.5571760
## 289 290 291 292 293 294 295 296
## 0.7248039 0.4394888 0.7248039 0.6139339 0.5173388 0.4390670 0.4240531 0.3185567
## 297 298 299 300 301 302 303 304
## 0.3393559 0.3974265 0.4686839 0.3847011 0.5867816 0.4655383 0.4929947 0.2635454
## 305 306 307 308 309 310 311 312
## 0.1990508 0.2494653 0.2341923 0.3070697 0.3127845 0.2583920 0.2494653 0.2439127
## 313 314 315 316 317 318 319 320
## 0.3127845 0.2390186 0.7114297 0.6946979 0.4721925 0.5431691 0.8177419 0.5431691
## 321 322 323 324 325 326 327 328
## 0.8177419 0.3528796 0.2700079 0.3543238 0.2813083 0.2813083 0.8293866 0.8607789
## 329 330 331 332 333 334 335 336
## 0.3839535 0.3070697 0.9171898 0.9171898 0.2439127 0.6165200 0.8567721 0.9292067
## 337 338 339 340 341 342 343 344
## 0.9249543 0.5173388 0.9068723 0.8137659 0.5917838 0.8482845 0.4612506 0.4612506
## 345 346 347 348 349 350 351 352
## 0.7023208 0.5271585 0.9433153 0.9313495 0.6239612 0.4313669 0.6371457 0.4118218
## 353 354 355 356 357 358 359 360
## 0.3257714 0.9614807 0.8313703 0.9297158 0.8079519 0.8600200 0.6757366 0.4152374
## 361 362 363 364 365 366 367 368
## 0.3309677 0.5928960 0.3880402 0.6411227 0.4996745 0.9168393 0.4996745 0.2413247
## 369 370 371 372 373 374 375 376
## 0.2964757 0.9585933 0.5071441 0.2737604 0.8099051 0.2500573 0.7392861 0.8940645
## 377 378 379 380 381 382 383 384
## 0.8860828 0.9585933 0.9839576 0.6788149 0.3925509 0.5463031 0.3925509 0.3925509
## 385 386 387 388 389 390 391 392
## 0.3090901 0.4421817 0.3090901 0.2841644 0.3090901 0.4796451 0.9625722 0.5463031
## 393 394 395 396 397 398 399 400
## 0.2482841 0.1911554 0.3699197 0.9406467 0.3453720 0.6946979 0.6946979 0.2488742
## 401 402 403 404 405 406 407 408
## 0.3453720 0.5333876 0.5490094 0.5622038 0.2217541 0.5455200 0.6490182 0.6367497
## 409 410 411 412 413 414 415 416
## 0.7802208 0.3007488 0.3070697 0.3070697 0.2753069 0.8810596 0.3014136 0.5451612
## 417 418 419 420 421 422 423 424
## 0.8284906 0.4623954 0.6209915 0.4406237 0.8659644 0.8732610 0.5392469 0.8161081
## 425 426 427 428 429 430 431 432
## 0.5392469 0.8732610 0.6920111 0.5416009 0.2545017 0.5165499 0.8161081 0.2166403
## 433 434 435 436 437 438 439 440
## 0.9363873 0.2384445 0.5799355 0.2384445 0.4382890 0.7419915 0.5799355 0.2545017
## 441 442 443 444 445 446 447 448
## 0.5079337 0.7709197 0.7520894 0.8007646 0.9471370 0.4129379 0.6265210 0.7714771
## 449 450 451 452 453 454 455 456
## 0.4382890 0.6243006 0.6243006 0.4867453 0.4332726 0.7807620 0.7052569 0.9682761
## 457 458 459 460 461 462 463 464
## 0.4772800 0.3063979 0.7807620 0.1875389 0.7362300 0.2596047 0.8242030 0.1717941
## 465 466 467 468 469 470 471 472
## 0.2867420 0.5420260 0.8216907 0.9867184 0.2450798 0.3521585 0.5745388 0.8017708
## 473 474 475 476 477 478 479 480
## 0.6579303 0.6859754 0.4511281 0.4647523 0.4030824 0.8718855 0.4647523 0.5055647
## 481 482 483 484 485 486 487 488
## 0.2653891 0.9063373 0.5575985 0.5641795 0.9657191 0.3326894 0.3326894 0.3037189
## 489 490 491 492 493 494 495 496
## 0.7918629 0.5645352 0.6131848 0.9729312 0.9816496 0.8435669 0.8450031 0.6946979
## 497 498 499 500 501 502 503 504
## 0.2222998 0.7858338 0.6946979 0.8435669 0.2222998 0.9163563 0.9163563 0.8532807
## 505 506 507 508 509 510 511 512
## 0.8433758 0.9385097 0.8324932 0.3604605 0.4847392 0.7887215 0.3014136 0.4847392
## 513 514 515 516 517 518 519 520
## 0.4753431 0.8427312 0.8427312 0.5910204 0.8803958 0.1269607 0.8359516 0.7630632
## 521 522 523 524 525 526 527 528
## 0.6113413 0.2358964 0.6191621 0.2488742 0.3185567 0.4340485 0.7630632 0.8260266
## 529 530 531 532 533 534 535 536
## 0.2147921 0.2589979 0.6646230 0.4973051 0.4973051 0.9379605 0.3880402 0.6646230
## 537 538 539 540 541 542 543 544
## 0.2589979 0.6967044 0.8683149 0.7210069 0.3597325 0.7380664 0.3824601 0.7328678
## 545 546 547 548 549 550 551 552
## 0.1823409 0.2126684 0.4046036 0.7450040 0.6146824 0.2800326 0.2740481 0.4996745
## 553 554 555 556 557 558 559 560
## 0.5063544 0.7791354 0.7624915 0.7624915 0.7871378 0.7624915 0.7871378 0.9349693
## 561 562 563 564 565 566 567 568
## 0.7356160 0.2258470 0.2401698 0.4456583 0.9349693 0.7356160 0.2000601 0.2000601
## 569 570 571 572 573 574 575 576
## 0.7078769 0.7915806 0.8200844 0.7915806 0.5232195 0.2951595 0.4796451 0.5970512
## 577 578 579 580 581 582 583 584
## 0.3629827 0.2958172 0.3644450 0.3107508 0.2330610 0.2330610 0.1634573 0.4274965
## 585 586 587 588 589 590 591 592
## 0.6910285 0.3536013 0.8015407 0.2494653 0.9848486 0.6327520 0.3230019 0.7190963
## 593 594 595 596 597 598 599 600
## 0.3230019 0.1916444 0.2583920 0.2395937 0.2545017 0.4332726 0.2938467 0.3084158
## 601 602 603 604 605 606 607 608
## 0.3326894 0.1835015 0.2161046 0.1835015 0.2263999 0.2305531 0.9198980 0.4957255
## 609 610 611 612 613 614 615 616
## 0.5431691 0.9036208 0.2077082 0.2439127 0.4137040 0.4546178 0.8687060 0.3230019
## 617 618 619 620 621 622 623 624
## 0.3230019 0.5807049 0.5314545 0.3681116 0.2800326 0.2854515 0.2687643 0.9422144
## 625 626 627 628 629 630 631 632
## 0.5447366 0.5447366 0.1713450 0.1713450 0.2545017 0.2596047 0.2545017 0.3127845
## 633 634 635 636 637 638 639 640
## 0.5224313 0.7514999 0.2848075 0.3114279 0.2390186 0.2390186 0.4980949 0.8800908
## 641 642 643 644 645 646 647 648
## 0.2854515 0.3127845 0.2854515 0.3127845 0.2854515 0.4988847 0.4588963 0.6536515
## 649 650 651 652 653 654 655 656
## 0.7470738 0.7149568 0.2629327 0.7470738 0.9953910 0.8271397 0.2241942 0.2800326
## 657 658 659 660 661 662 663 664
## 0.2629327 0.5947690 0.4495640 0.6475775 0.4495640 0.2177145 0.3783959 0.5906064
## 665 666 667 668 669 670 671 672
## 0.5047750 0.3171866 0.2958172 0.2653891 0.6475775 0.2653891 0.4008041 0.2533047
## 673 674 675 676 677 678 679 680
## 0.2583920 0.2533047 0.2915849 0.4980949 0.2915849 0.3158198 0.3691836 0.2841644
## 681 682 683 684 685 686 687 688
## 0.1571155 0.3928959 0.3281755 0.7094801 0.2155699 0.7094801 0.2171769 0.3347969
## 689 690 691 692 693 694 695 696
## 0.2110858 0.5575985 0.5540558 0.3281755 0.5342400 0.2854515 0.2800326 0.9314419
## 697 698 699 700 701 702 703 704
## 0.3362055 0.3362055 0.3000848 0.6305469 0.4194303 0.3362055 0.3134640 0.3783959
## 705 706 707 708 709 710 711 712
## 0.3057269 0.3216218 0.3326894 0.4823723 0.7921013 0.4530516 0.4194303 0.3014136
## 713 714 715 716 717 718 719 720
## 0.2023236 0.2066703 0.3107508 0.1825567 0.2066703 0.3944041 0.2330610 0.2635454
## 721 722 723 724 725 726 727 728
## 0.2330610 0.2033452 0.4072381 0.8650915 0.7101308 0.4831612 0.7526780 0.3421947
## 729 730 731 732 733 734 735 736
## 0.3421947 0.8964099 0.3597325 0.7297640 0.2476949 0.2958172 0.2647736 0.2433305
## 737 738 739 740 741 742 743 744
## 0.2433305 0.2806700 0.2166403 0.2545017 0.8054889 0.2793961 0.2602124 0.3691836
## 745 746 747 748 749 750 751 752
## 0.2482841 0.3728699 0.1501671 0.2155699 0.3910452 0.3728699 0.2687643 0.2687643
## 753 754 755 756 757 758 759 760
## 0.2800326 0.2687643 0.4867453 0.4807948 0.6275981 0.3050568 0.3050568 0.1990508
## 761 762 763 764 765 766 767 768
## 0.3230019 0.2236452 0.3691836 0.2236452 0.2687643 0.2635454 0.2439127 0.3070697
## 769 770 771 772 773 774 775 776
## 0.3000848 0.2841644 0.3000848 0.2488742 0.2488742 0.4969434 0.4902644 0.3267839
## 777 778 779 780 781 782 783 784
## 0.4906259 0.4839501 0.5490094 0.3644450 0.2746771 0.3959143 0.3691836 0.3959143
## 785 786 787 788 789 790 791 792
## 0.3582785 0.2433305 0.2433305 0.4129379 0.4129379 0.2161046 0.3902932 0.2841644
## 793 794 795 796 797 798 799 800
## 0.3407739 0.5924826 0.8866923 0.8035433 0.4038428 0.7392861 0.5408164 0.5408164
## 801 802 803 804 805 806 807 808
## 0.2294341 0.3151375 0.9725578 0.3536013 0.3699197 0.9438966 0.9267882 0.9438966
## 809 810 811 812 813 814 815 816
## 0.3813728 0.4573277 0.4557600 0.6896778 0.6846127 0.7407800 0.8064770 0.6846127
## 817 818 819 820 821 822 823 824
## 0.5290937 0.8775534 0.2023236 0.2841644 0.3407739 0.9848486 0.3895417 0.3895417
## 825 826 827 828 829 830 831 832
## 0.4440979 0.5208546 0.6890013 0.5208546 0.9440637 0.7767022 0.6109344 0.7767022
## 833 834 835 836 837 838 839 840
## 0.5602916 0.5602916 0.2110858 0.2116124 0.7013013 0.7013013 0.8191503 0.3326894
## 841 842 843 844 845 846 847 848
## 0.9075261 0.5106639 0.7120779 0.2854515 0.7085298 0.4023225 0.4023225 0.4538346
## 849 850 851 852 853 854 855 856
## 0.4023225 0.4023225 0.2687643 0.2687643 0.6447490 0.7175251 0.7175251 0.8466510
## 857 858 859 860 861 862 863 864
## 0.7175251 0.8146814 0.6131848 0.8151579 0.2635454 0.7698019 0.4106422 0.3000848
## 865 866 867 868 869 870 871 872
## 0.2635454 0.3057269 0.8340111 0.8230751 0.8151579 0.6663516 0.8081763 0.5482271
## 873 874 875 876 877 878 879 880
## 0.3754281 0.6767450 0.8391573 0.8835066 0.6298106 0.8081763 0.3507183 0.2687643
## 881 882 883 884 885 886 887 888
## 0.3216218 0.7750541 0.9008578 0.2687643 0.3507183 0.5687694 0.5497915 0.9119144
## 889 890 891 892 893 894 895 896
## 0.6876457 0.2971351 0.4259508 0.3230019 0.2806700 0.3230019 0.3521585 0.5802879
## 897 898 899 900 901 902 903 904
## 0.9369495 0.5802879 0.9369495 0.5973993 0.8530662 0.6729615 0.6729615 0.6116850
## 905 906 907 908 909 910 911 912
## 0.6116850 0.2902815 0.7276042 0.7401729 0.6116850 0.7297640 0.9473671 0.6421817
## 913 914 915 916 917 918 919 920
## 0.8793942 0.8849483 0.7297640 0.8592576 0.6715693 0.4655383 0.7328678 0.9083184
## 921 922 923 924 925 926 927 928
## 0.5157609 0.7328678 0.9083184 0.4655383 0.8069696 0.8037716 0.6593509 0.2305531
## 929 930 931 932 933 934 935 936
## 0.8069696 0.8882697 0.6265210 0.3959143 0.2902815 0.3959143 0.6265210 0.8628325
## 937 938 939 940 941 942 943 944
## 0.8628325 0.5165499 0.9516618 0.7197341 0.9287899 0.8839798 0.4398452 0.3407739
## 945 946 947 948 949 950 951 952
## 0.8739587 0.7858338 0.9409181 0.8997234 0.8859098 0.8859098 0.8859098 0.8997234
## 953 954 955 956 957 958 959 960
## 0.7520894 0.8879558 0.8548227 0.5629813 0.7413862 0.6767450 0.7847685 0.4201998
## 961 962 963 964 965 966 967 968
## 0.8115814 0.2647736 0.3482347 0.7570379 0.8115814 0.7995229 0.8275909 0.1825567
## 969 970 971 972 973 974 975 976
## 0.8736103 0.4714052 0.7868953 0.7868953 0.8091539 0.6124352 0.8679532 0.2341923
## 977 978 979 980 981 982 983 984
## 0.2341923 0.1751724 0.6694755 0.5365314 0.6139339 0.4267234 0.9625722 0.6139339
## 985 986 987 988 989 990 991 992
## 0.5365314 0.7203709 0.7250924 0.2247442 0.2201228 0.5852487 0.2201228 0.2155699
## 993 994 995 996 997 998 999 1000
## 0.2155699 0.2155699 0.1938357 0.4522689 0.9331287 0.9331287 0.1675843 0.9221762
## 1001 1002 1003 1004 1005 1006 1007 1008
## 0.8216907 0.7029809 0.8865469 0.9184901 0.4507041 0.9184901 0.8865469 0.8865469
## 1009 1010 1011 1012 1013 1014 1015 1016
## 0.8800628 0.6896778 0.8702740 0.4831612 0.3386479 0.4008041 0.6990078 0.8512920
## 1017 1018 1019 1020 1021 1022 1023 1024
## 0.9398555 0.8833302 0.8833302 0.5232195 0.8076861 0.8076861 0.5024057 0.8702740
## 1025 1026 1027 1028 1029 1030 1031 1032
## 0.5157609 0.5165499 0.9038956 0.7497257 0.4000456 0.5157609 0.7452787 0.6536515
## 1033 1034 1035 1036 1037 1038 1039 1040
## 0.2787605 0.5098744 0.2675243 0.6004349 0.9018292 0.2166403 0.9428063 0.8032728
## 1041 1042 1043 1044 1045 1046 1047 1048
## 0.5114534 0.3895417 0.8032728 0.7587769 0.8793942 0.7407800 0.6060740 0.5431691
## 1049 1050 1051 1052 1053 1054 1055 1056
## 0.7438017 0.7181650 0.5431691 0.6897347 0.9259261 0.9481492 0.2263999 0.2263999
## 1057 1058 1059 1060 1061 1062 1063 1064
## 0.8584917 0.2038575 0.8504904 0.8584917 0.4538346 0.9230783 0.7703613 0.8318127
## 1065 1066 1067 1068 1069 1070 1071 1072
## 0.8081763 0.4647523 0.9285807 0.7684229 0.7684229 0.6305469 0.8273844 0.2258470
## 1073 1074 1075 1076 1077 1078 1079 1080
## 0.2848075 0.5802879 0.2258470 0.6349517 0.8101278 0.6536515 0.6536515 0.8577224
## 1081 1082 1083 1084 1085 1086 1087 1088
## 0.7820883 0.8577224 0.2902815 0.8984456 0.2902815 0.2577871 0.8132462 0.7570379
## 1089 1090 1091 1092 1093 1094 1095 1096
## 0.4937844 0.4937844 0.8318127 0.7509094 0.7650327 0.9183817 0.5466617 0.4757039
## 1097 1098 1099 1100 1101 1102 1103 1104
## 0.5466617 0.2341923 0.9429765 0.2341923 0.9322445 0.8588751 0.6760535 0.8588751
## 1105 1106 1107 1108 1109 1110 1111 1112
## 0.9089630 0.9279496 0.8672274 0.8892069 0.2384445 0.7127252 0.4398452 0.8950948
## 1113 1114 1115 1116 1117 1118 1119 1120
## 0.7276042 0.2902815 0.9719614 0.7310082 0.7310082 0.7310082 0.9472094 0.9312399
## 1121 1122 1123 1124 1125 1126 1127 1128
## 0.9603587 0.8702740 0.8476565 0.7368430 0.7791354 0.7257217 0.9577651 0.7698019
## 1129 1130 1131 1132 1133 1134 1135 1136
## 0.4663244 0.6011926 0.5365314 0.2150362 0.9842276 0.7520894 0.8690357 0.8350833
## 1137 1138 1139 1140 1141 1142 1143 1144
## 0.8221531 0.8221531 0.2082286 0.3185567 0.3243851 0.7952056 0.7750541 0.5595132
## 1145 1146 1147 1148 1149 1150 1151 1152
## 0.4522689 0.6518914 0.5412416 0.7728433 0.8132462 0.8193645 0.9518069 0.9151375
## 1153 1154 1155 1156 1157 1158 1159 1160
## 0.3223114 0.7216419 0.7887215 0.3223114 0.8749990 0.9670991 0.8544633 0.6610929
## 1161 1162 1163 1164 1165 1166 1167 1168
## 0.7526780 0.5645352 0.8596392 0.4596809 0.4596809 0.5902565 0.7056127 0.9472950
## 1169 1170 1171 1172 1173 1174 1175 1176
## 0.8346477 0.7791354 0.5883128 0.6670537 0.9176684 0.3070697 0.3070697 0.8137256
## 1177 1178 1179 1180 1181 1182 1183 1184
## 0.6522196 0.9316434 0.8101278 0.7772497 0.7772497 0.6746682 0.7036401 0.2545017
## 1185 1186 1187 1188 1189 1190 1191 1192
## 0.7009982 0.7769531 0.8410491 0.7769531 0.7009982 0.2330610 0.8969952 0.5598697
## 1193 1194 1195 1196 1197 1198 1199 1200
## 0.9355432 0.5598697 0.2902815 0.2958172 0.2848075 0.3281755 0.7488605 0.2848075
## 1201 1202 1203 1204 1205 1206 1207 1208
## 0.3281755 0.6760535 0.8395832 0.2488742 0.7952056 0.7952056 0.7952056 0.6364150
## 1209 1210 1211 1212 1213 1214 1215 1216
## 0.7952056 0.8344480 0.5298808 0.2647736 0.5298808 0.6389391 0.4367341 0.7094801
## 1217 1218 1219 1220 1221 1222 1223 1224
## 0.3482347 0.9077909 0.6257814 0.7684229 0.8404324 0.8404324 0.2427493 0.8959814
## 1225 1226 1227 1228 1229 1230 1231 1232
## 0.6342191 0.2294341 0.1786029 0.3910452 0.9766328 0.3185567 0.8807281 0.6305469
## 1233 1234 1235 1236 1237 1238 1239 1240
## 0.3185567 0.2028339 0.8830042 0.7503180 0.2964757 0.8830042 0.2427493 0.8039997
## 1241 1242 1243 1244 1245 1246 1247 1248
## 0.1667047 0.3582785 0.8736103 0.3070697 0.4507041 0.5947690 0.3386479 0.5947690
## 1249 1250 1251 1252 1253 1254 1255 1256
## 0.7672967 0.6482982 0.6482982 0.3582785 0.3521585 0.2848075 0.6953675 0.5714468
## 1257 1258 1259 1260 1261 1262 1263 1264
## 0.1598363 0.7222761 0.7464763 0.7464763 0.5974632 0.6818778 0.3521585 0.2427493
## 1265 1266 1267 1268 1269 1270 1271 1272
## 0.8628325 0.4906259 0.4906259 0.8611571 0.2687643 0.9877289 0.9852422 0.7915806
## 1273 1274 1275 1276 1277 1278 1279 1280
## 0.6701742 0.1781399 0.6701742 0.2635454 0.8346477 0.3407739 0.2635454 0.7552904
## 1281 1282 1283 1284 1285 1286 1287 1288
## 0.5431691 0.5431691 0.6896778 0.4847392 0.6172666 0.5224313 0.8787225 0.8955265
## 1289 1290 1291 1292 1293 1294 1295 1296
## 0.7992909 0.7992909 0.6596758 0.7282299 0.9111501 0.2781257 0.7282299 0.2195809
## 1297 1298 1299 1300 1301 1302 1303 1304
## 0.2195809 0.8648605 0.8613300 0.5126042 0.9064601 0.8567721 0.8069696 0.7419915
## 1305 1306 1307 1308 1309 1310 1311 1312
## 0.1671440 0.3223114 0.4522689 0.6407285 0.4522689 0.2401698 0.3223114 0.9261425
## 1313 1314 1315 1316 1317 1318 1319 1320
## 0.4557600 0.4000456 0.3936498 0.3521585 0.9018292 0.8655973 0.3521585 0.6611524
## 1321 1322 1323 1324 1325 1326 1327 1328
## 0.4788566 0.9018292 0.7250924 0.7818417 0.5679943 0.5679943 0.5679943 0.5679943
## 1329 1330 1331 1332 1333 1334 1335 1336
## 0.2161046 0.3400645 0.3400645 0.1747164 0.2471067 0.2681439 0.3629827 0.9247347
## 1337 1338 1339 1340 1341 1342 1343 1344
## 0.2195809 0.2195809 0.2195809 0.5024057 0.5024057 0.5024057 0.4565438 0.5024057
## 1345 1346 1347 1348 1349 1350 1351 1352
## 0.6228174 0.4186612 0.7303866 0.1933425 0.1933425 0.5664433 0.3944041 0.7820883
## 1353 1354 1355 1356 1357 1358 1359 1360
## 0.3985300 0.3985300 0.3629827 0.3379407 0.3379407 0.6825627 0.2433305 0.4549985
## 1361 1362 1363 1364 1365 1366 1367 1368
## 0.6172666 0.4000456 0.4549985 0.2938467 0.7910589 0.3070697 0.3050568 0.8480982
## 1369 1370 1371 1372 1373 1374 1375 1376
## 0.3050568 0.3662455 0.5853132 0.9212837 0.5853132 0.1860991 0.3107508 0.3393559
## 1377 1378 1379 1380 1381 1382 1383 1384
## 0.3267839 0.8793942 0.3333912 0.6305469 0.6305469 0.2378713 0.3216218 0.3216218
## 1385 1386 1387 1388 1389 1390 1391 1392
## 0.2433305 0.1598363 0.3063979 0.3063979 0.5439530 0.3000848 0.9018292 0.6536515
## 1393 1394 1395 1396 1397 1398 1399 1400
## 0.2938467 0.3865408 0.1825567 0.3358531 0.3358531 0.3393559 0.4780683 0.7526780
## 1401 1402 1403 1404 1405 1406 1407 1408
## 0.3333912 0.3333912 0.9011396 0.7285687 0.6060740 0.8583159 0.8165817 0.7788863
## 1409 1410 1411 1412 1413 1414 1415 1416
## 0.9621767 0.7788863 0.7675549 0.6504562 0.8165817 0.6356836 0.4298177 0.4183091
## 1417 1418 1419 1420 1421 1422 1423 1424
## 0.4298177 0.9075261 0.2828809 0.2987594 0.2828809 0.2641590 0.8512920 0.7059132
## 1425 1426 1427 1428 1429 1430 1431 1432
## 0.3783959 0.3783959 0.8931635 0.6990078 0.6832468 0.8677873 0.5298808 0.3514380
## 1433 1434 1435 1436 1437 1438 1439 1440
## 0.8892069 0.5322411 0.2825875 0.2825875 0.1454259 0.6529359 0.4049522 0.7254159
## 1441 1442 1443 1444 1445 1446 1447 1448
## 0.7936578 0.2666054 0.4612506 0.7541207 0.7322489 0.2734200 0.4612506 0.4290436
## 1449 1450 1451 1452 1453 1454 1455 1456
## 0.4225107 0.8064770 0.7936578 0.7630632 0.8504904 0.1916444 0.6953675 0.6603847
## 1457 1458 1459 1460 1461 1462 1463 1464
## 0.4886522 0.1916444 0.5423851 0.8962755 0.5695441 0.4201998 0.5031954 0.5729935
## 1465 1466 1467 1468 1469 1470 1471 1472
## 0.3839535 0.3839535 0.4464390 0.6475775 0.4464390 0.3114279 0.2675243 0.8942015
## 1473 1474 1475 1476 1477 1478 1479 1480
## 0.7858338 0.6290737 0.2328025 0.9836817 0.2328025 0.9842276 0.4121723 0.6195032
## 1481 1482 1483 1484 1485 1486 1487 1488
## 0.4522689 0.6195032 0.4706180 0.5996767 0.6663516 0.3114279 0.4906259 0.6801910
## 1489 1490 1491 1492 1493 1494 1495 1496
## 0.7407800 0.7127252 0.8843034 0.7407800 0.8195982 0.3347969 0.4796451 0.6973715
## 1497 1498 1499 1500 1501 1502 1503 1504
## 0.3347969 0.7836993 0.6053195 0.7836993 0.2787605 0.2390186 0.3281755 0.7168843
## 1505 1506 1507 1508 1509 1510 1511 1512
## 0.7250924 0.4205847 0.6550805 0.7250924 0.6443566 0.7425958 0.4487824 0.4087642
## 1513 1514 1515 1516 1517 1518 1519 1520
## 0.5497915 0.5423851 0.3050755 0.3087528 0.7794299 0.6403954 0.5622038 0.4514864
## 1521 1522 1523 1524 1525 1526 1527 1528
## 0.6403954 0.1432798 0.7794299 0.5165499 0.5330275 0.3651771 0.3475180 0.6550805
## 1529 1530 1531 1532 1533 1534 1535 1536
## 0.4855283 0.4647523 0.7756045 0.5047750 0.4406237 0.2043707 0.8127660 0.3347969
## 1537 1538 1539 1540 1541 1542 1543 1544
## 0.5439530 0.4914155 0.8588751 0.5071441 0.8007646 0.7334859 0.5799031 0.4965153
## 1545 1546 1547 1548 1549 1550 1551 1552
## 0.8110978 0.4655383 0.4714052 0.8304826 0.5031954 0.7974896 0.2583920 0.2439127
## 1553 1554 1555 1556 1557 1558 1559 1560
## 0.7613454 0.2746771 0.6656489 0.6072049 0.3637135 0.6656489 0.2740481 0.3393559
## 1561 1562 1563 1564 1565 1566 1567 1568
## 0.3393559 0.3393559 0.4000456 0.4000456 0.4000456 0.7636339 0.7667321 0.4000456
## 1569 1570 1571 1572 1573 1574 1575 1576
## 0.2305531 0.7552904 0.9535805 0.7162427 0.2482841 0.9191968 0.4623954 0.8355179
## 1577 1578 1579 1580 1581 1582 1583 1584
## 0.7235418 0.8350833 0.7661666 0.7310082 0.8350833 0.7310082 0.7949265 0.3769108
## 1585 1586 1587 1588 1589 1590 1591 1592
## 0.8635786 0.8639503 0.8546431 0.6781257 0.9541999 0.2077082 0.8697532 0.7692416
## 1593 1594 1595 1596 1597 1598 1599
## 0.7482658 0.4305921 0.5290937 0.7897725 0.7482658 0.5381023 0.7003355library(caret)
library(pROC)## Warning: package 'pROC' was built under R version 4.3.2## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, varpdataF <- as.factor(ifelse(test=as.numeric(pdata>0.5) == 0, yes="good", no="bad"))
attributes(pdataF)## $levels
## [1] "bad" "good"
##
## $class
## [1] "factor"- Model Insights (10 points): (Regression)
Model Accuracy
Accuracy: 0.2964
Percentage of correctly predicted observations.
95% CI: (0.2741, 0.3195)
The confidence interval for the accuracy.
No Information Rate (NIR): 0.5347
The accuracy that could be achieved by always predicting the majority class.
P-Value [Acc > NIR]: 1.00000
The probability that the accuracy is greater than the No Information Rate.
Kappa: -0.4065
A measure of how closely the instances classified as the same class agree with the actual classification.
A kappa of 1 represents perfect agreement, and a kappa of 0 represents agreement equivalent to random chance.
Negative values suggest no agreement.
Mcnemar’s Test P-Value: 0.07364
A statistical test used on paired nominal data.
Tests if the row and column marginal frequencies are equal.
Sensitivity (True Positive Rate): 0.2849
The proportion of actual positive cases that were correctly identified.
Specificity (True Negative Rate): 0.3064
The proportion of actual negative cases that were correctly identified.
Positive Predictive Value (PPV): 0.2634 The proportion of positive identifications that were actually correct.
Negative Predictive Value (NPV): 0.33
The proportion of negative identifications that were actually correct.
Prevalence: 0.4653
The proportion of actual positive cases.
Detection Rate: 0.1326
The proportion of actual positive cases
# Create confusion matrix
confusionMatrix(pdataF, df$quality)## Confusion Matrix and Statistics
##
## Reference
## Prediction bad good
## bad 220 593
## good 524 262
##
## Accuracy : 0.3014
## 95% CI : (0.279, 0.3246)
## No Information Rate : 0.5347
## P-Value [Acc > NIR] : 1.00000
##
## Kappa : -0.3955
##
## Mcnemar's Test P-Value : 0.04189
##
## Sensitivity : 0.2957
## Specificity : 0.3064
## Pos Pred Value : 0.2706
## Neg Pred Value : 0.3333
## Prevalence : 0.4653
## Detection Rate : 0.1376
## Detection Prevalence : 0.5084
## Balanced Accuracy : 0.3011
##
## 'Positive' Class : bad
## # From pROC
roc(df$quality,logistic$fitted.values,plot=TRUE)## Setting levels: control = bad, case = good## Setting direction: controls < cases##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, plot = TRUE)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 0.7792par(pty = "s")
roc(df$quality,logistic$fitted.values,plot=TRUE)## Setting levels: control = bad, case = good
## Setting direction: controls < cases
##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, plot = TRUE)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 0.7792## NOTE: By default, roc() uses specificity on the x-axis and the values range
## from 1 to 0. This makes the graph look like what we would expect, but the
## x-axis itself might induce a headache. To use 1-specificity (i.e. the
## False Positive Rate) on the x-axis, set "legacy.axes" to TRUE.
roc(df$quality,logistic$fitted.values,plot=TRUE, legacy.axes=TRUE)## Setting levels: control = bad, case = good## Setting direction: controls < cases
##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, plot = TRUE, legacy.axes = TRUE)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 0.7792roc(df$quality,logistic$fitted.values,plot=TRUE, legacy.axes=TRUE, xlab="False Positive Percentage", ylab="True Postive Percentage")## Setting levels: control = bad, case = good
## Setting direction: controls < cases
##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage", ylab = "True Postive Percentage")
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 0.7792roc(df$quality,logistic$fitted.values,plot=TRUE, legacy.axes=TRUE, xlab="False Positive Percentage", ylab="True Postive Percentage", col="#377eb8", lwd=4)## Setting levels: control = bad, case = good## Setting direction: controls < cases##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage", ylab = "True Postive Percentage", col = "#377eb8", lwd = 4)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 0.7792roc(df$quality,logistic$fitted.values,plot=TRUE, legacy.axes=TRUE, xlab="False Positive Percentage", ylab="True Postive Percentage", col="#377eb8", lwd=4)## Setting levels: control = bad, case = good
## Setting direction: controls < cases
##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage", ylab = "True Postive Percentage", col = "#377eb8", lwd = 4)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 0.7792## If we want to find out the optimal threshold we can store the
## data used to make the ROC graph in a variable...
roc.info <- roc(df$quality, logistic$fitted.values, legacy.axes=TRUE)## Setting levels: control = bad, case = good## Setting direction: controls < casesstr(roc.info)## List of 15
## $ percent : logi FALSE
## $ sensitivities : num [1:890] 1 1 1 1 1 ...
## $ specificities : num [1:890] 0 0.00134 0.00269 0.00403 0.00538 ...
## $ thresholds : num [1:890] -Inf 0.123 0.135 0.144 0.148 ...
## $ direction : chr "<"
## $ cases : Named num [1:855] 0.352 0.333 0.279 0.639 0.526 ...
## ..- attr(*, "names")= chr [1:855] "4" "8" "9" "17" ...
## $ controls : Named num [1:744] 0.254 0.415 0.396 0.254 0.254 ...
## ..- attr(*, "names")= chr [1:744] "1" "2" "3" "5" ...
## $ fun.sesp :function (thresholds, controls, cases, direction)
## $ auc : 'auc' num 0.779
## ..- attr(*, "partial.auc")= logi FALSE
## ..- attr(*, "percent")= logi FALSE
## ..- attr(*, "roc")=List of 15
## .. ..$ percent : logi FALSE
## .. ..$ sensitivities : num [1:890] 1 1 1 1 1 ...
## .. ..$ specificities : num [1:890] 0 0.00134 0.00269 0.00403 0.00538 ...
## .. ..$ thresholds : num [1:890] -Inf 0.123 0.135 0.144 0.148 ...
## .. ..$ direction : chr "<"
## .. ..$ cases : Named num [1:855] 0.352 0.333 0.279 0.639 0.526 ...
## .. .. ..- attr(*, "names")= chr [1:855] "4" "8" "9" "17" ...
## .. ..$ controls : Named num [1:744] 0.254 0.415 0.396 0.254 0.254 ...
## .. .. ..- attr(*, "names")= chr [1:744] "1" "2" "3" "5" ...
## .. ..$ fun.sesp :function (thresholds, controls, cases, direction)
## .. ..$ auc : 'auc' num 0.779
## .. .. ..- attr(*, "partial.auc")= logi FALSE
## .. .. ..- attr(*, "percent")= logi FALSE
## .. .. ..- attr(*, "roc")=List of 8
## .. .. .. ..$ percent : logi FALSE
## .. .. .. ..$ sensitivities: num [1:890] 1 1 1 1 1 ...
## .. .. .. ..$ specificities: num [1:890] 0 0.00134 0.00269 0.00403 0.00538 ...
## .. .. .. ..$ thresholds : num [1:890] -Inf 0.123 0.135 0.144 0.148 ...
## .. .. .. ..$ direction : chr "<"
## .. .. .. ..$ cases : Named num [1:855] 0.352 0.333 0.279 0.639 0.526 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:855] "4" "8" "9" "17" ...
## .. .. .. ..$ controls : Named num [1:744] 0.254 0.415 0.396 0.254 0.254 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:744] "1" "2" "3" "5" ...
## .. .. .. ..$ fun.sesp :function (thresholds, controls, cases, direction)
## .. .. .. ..- attr(*, "class")= chr "roc"
## .. ..$ call : language roc.default(response = df$quality, predictor = logistic$fitted.values, legacy.axes = TRUE)
## .. ..$ original.predictor: Named num [1:1599] 0.254 0.415 0.396 0.352 0.254 ...
## .. .. ..- attr(*, "names")= chr [1:1599] "1" "2" "3" "4" ...
## .. ..$ original.response : Factor w/ 2 levels "bad","good": 1 1 1 2 1 1 1 2 2 1 ...
## .. ..$ predictor : Named num [1:1599] 0.254 0.415 0.396 0.352 0.254 ...
## .. .. ..- attr(*, "names")= chr [1:1599] "1" "2" "3" "4" ...
## .. ..$ response : Factor w/ 2 levels "bad","good": 1 1 1 2 1 1 1 2 2 1 ...
## .. ..$ levels : chr [1:2] "bad" "good"
## .. ..- attr(*, "class")= chr "roc"
## $ call : language roc.default(response = df$quality, predictor = logistic$fitted.values, legacy.axes = TRUE)
## $ original.predictor: Named num [1:1599] 0.254 0.415 0.396 0.352 0.254 ...
## ..- attr(*, "names")= chr [1:1599] "1" "2" "3" "4" ...
## $ original.response : Factor w/ 2 levels "bad","good": 1 1 1 2 1 1 1 2 2 1 ...
## $ predictor : Named num [1:1599] 0.254 0.415 0.396 0.352 0.254 ...
## ..- attr(*, "names")= chr [1:1599] "1" "2" "3" "4" ...
## $ response : Factor w/ 2 levels "bad","good": 1 1 1 2 1 1 1 2 2 1 ...
## $ levels : chr [1:2] "bad" "good"
## - attr(*, "class")= chr "roc"## tpp = true positive percentage
## fpp = false positive precentage
roc.df <- data.frame(tpp=roc.info$sensitivities*100, fpp=(1 - roc.info$specificities)*100,thresholds=roc.info$thresholds)
head(roc.df) ## tpp fpp thresholds
## 1 100.00000 100.00000 -Inf
## 2 100.00000 99.86559 0.1228900
## 3 100.00000 99.73118 0.1351202
## 4 100.00000 99.59677 0.1443528
## 5 100.00000 99.46237 0.1477965
## 6 99.88304 99.46237 0.1518881## head() will show us the values for the upper right-hand corner of the ROC graph, when the threshold is so low
## (negative infinity) that every single sample is called "obese".
## Thus TPP = 100% and FPP = 100%
tail(roc.df) ## tpp fpp thresholds
## 885 0.8187135 0.1344086 0.9845381
## 886 0.5847953 0.1344086 0.9850454
## 887 0.4678363 0.1344086 0.9859803
## 888 0.1169591 0.1344086 0.9872237
## 889 0.0000000 0.1344086 0.9915600
## 890 0.0000000 0.0000000 Inf## tail() will show us the values for the lower left-hand corner
## of the ROC graph, when the threshold is so high (infinity)
## that every single sample is called "not obese".
## Thus, TPP = 0% and FPP = 0%
## now let's look at the thresholds between TPP 60% and 80%
roc.df[roc.df$tpp > 60 & roc.df$tpp < 80,]## tpp fpp thresholds
## 252 79.64912 38.03763 0.4133210
## 253 79.41520 38.03763 0.4144707
## 254 79.29825 37.90323 0.4167733
## 255 79.29825 37.76882 0.4184852
## 256 79.18129 37.76882 0.4190458
## 257 79.06433 37.63441 0.4198151
## 258 78.94737 37.36559 0.4203923
## 259 78.94737 37.23118 0.4215477
## 260 78.83041 37.09677 0.4232819
## 261 78.71345 37.09677 0.4250019
## 262 78.71345 36.96237 0.4261601
## 263 78.71345 36.82796 0.4265465
## 264 78.71345 36.69355 0.4271099
## 265 78.59649 36.69355 0.4282700
## 266 78.59649 36.55914 0.4294306
## 267 78.59649 36.29032 0.4302049
## 268 78.47953 36.29032 0.4309795
## 269 78.36257 36.29032 0.4319319
## 270 78.36257 36.15591 0.4328848
## 271 78.12865 36.15591 0.4336606
## 272 78.12865 36.02151 0.4344367
## 273 78.01170 35.75269 0.4357794
## 274 77.89474 35.75269 0.4375116
## 275 77.66082 35.75269 0.4386780
## 276 77.54386 35.75269 0.4392779
## 277 77.54386 35.61828 0.4396670
## 278 77.30994 35.61828 0.4402345
## 279 77.19298 35.48387 0.4410131
## 280 77.19298 35.34946 0.4417921
## 281 77.07602 35.34946 0.4425714
## 282 76.95906 35.34946 0.4435295
## 283 76.84211 35.21505 0.4448781
## 284 76.72515 35.21505 0.4460486
## 285 76.49123 35.21505 0.4476107
## 286 76.37427 35.21505 0.4491732
## 287 76.14035 35.21505 0.4501341
## 288 76.02339 35.08065 0.4509161
## 289 75.90643 35.08065 0.4513072
## 290 75.78947 34.94624 0.4518776
## 291 75.67251 34.40860 0.4526602
## 292 75.55556 34.40860 0.4534431
## 293 75.08772 34.40860 0.4542262
## 294 75.08772 34.27419 0.4548081
## 295 74.85380 34.27419 0.4553792
## 296 74.85380 34.00538 0.4559724
## 297 74.85380 33.60215 0.4563643
## 298 74.73684 33.60215 0.4569358
## 299 74.61988 33.60215 0.4577198
## 300 74.61988 33.46774 0.4585041
## 301 74.61988 33.33333 0.4592886
## 302 74.61988 33.06452 0.4604657
## 303 74.38596 32.79570 0.4618230
## 304 74.26901 32.52688 0.4633939
## 305 74.03509 32.52688 0.4645723
## 306 73.80117 32.25806 0.4651453
## 307 73.33333 32.25806 0.4659313
## 308 73.33333 32.12366 0.4675041
## 309 73.33333 31.98925 0.4696509
## 310 73.33333 31.85484 0.4710116
## 311 73.21637 31.58602 0.4717988
## 312 73.21637 31.45161 0.4737678
## 313 73.09942 31.45161 0.4755235
## 314 73.09942 31.31720 0.4764920
## 315 73.09942 31.18280 0.4776741
## 316 72.98246 31.18280 0.4784624
## 317 72.98246 31.04839 0.4792509
## 318 72.63158 31.04839 0.4802199
## 319 72.51462 31.04839 0.4815835
## 320 72.51462 30.91398 0.4827667
## 321 72.28070 30.77957 0.4835556
## 322 72.16374 30.77957 0.4843446
## 323 71.69591 30.77957 0.4851337
## 324 71.57895 30.77957 0.4861368
## 325 71.34503 30.77957 0.4876988
## 326 71.22807 30.77957 0.4892443
## 327 71.11111 30.77957 0.4900504
## 328 70.99415 30.51075 0.4904452
## 329 70.64327 30.37634 0.4910207
## 330 70.52632 30.37634 0.4922051
## 331 70.52632 30.24194 0.4933896
## 332 70.29240 30.24194 0.4947550
## 333 70.17544 30.10753 0.4961204
## 334 70.05848 29.97312 0.4967294
## 335 69.94152 29.97312 0.4971243
## 336 69.94152 29.70430 0.4977000
## 337 69.82456 29.56989 0.4984898
## 338 69.70760 29.56989 0.4992796
## 339 69.35673 29.56989 0.5010401
## 340 68.88889 29.43548 0.5028005
## 341 68.77193 29.30108 0.5034095
## 342 68.77193 29.03226 0.5041993
## 343 68.77193 28.76344 0.5051698
## 344 68.65497 28.76344 0.5057455
## 345 68.65497 28.49462 0.5061404
## 346 68.53801 28.49462 0.5067493
## 347 68.53801 28.22581 0.5075389
## 348 68.42105 28.22581 0.5089041
## 349 68.30409 28.22581 0.5102692
## 350 68.30409 28.09140 0.5110586
## 351 68.18713 27.95699 0.5116673
## 352 68.18713 27.82258 0.5122427
## 353 68.18713 27.68817 0.5141826
## 354 67.95322 27.55376 0.5161554
## 355 67.71930 27.28495 0.5169444
## 356 67.60234 27.15054 0.5179470
## 357 67.60234 27.01613 0.5197049
## 358 67.60234 26.74731 0.5216430
## 359 67.48538 26.61290 0.5228254
## 360 67.48538 26.34409 0.5246149
## 361 67.36842 26.20968 0.5264041
## 362 67.36842 26.07527 0.5269782
## 363 67.36842 25.94086 0.5281261
## 364 67.01754 25.80645 0.5294872
## 365 66.78363 25.67204 0.5306676
## 366 66.78363 25.53763 0.5318478
## 367 66.66667 25.53763 0.5326343
## 368 66.43275 25.53763 0.5332076
## 369 66.31579 25.53763 0.5338138
## 370 66.31579 25.40323 0.5353857
## 371 66.19883 25.13441 0.5373169
## 372 66.19883 25.00000 0.5386746
## 373 66.19883 24.73118 0.5400317
## 374 65.96491 24.73118 0.5410290
## 375 65.84795 24.73118 0.5414213
## 376 65.73099 24.73118 0.5418134
## 377 65.73099 24.59677 0.5422055
## 378 65.61404 24.46237 0.5427771
## 379 65.02924 24.19355 0.5435611
## 380 64.91228 24.05914 0.5443448
## 381 64.79532 23.79032 0.5449489
## 382 64.79532 23.65591 0.5453406
## 383 64.67836 23.65591 0.5459115
## 384 64.44444 23.65591 0.5464824
## 385 64.21053 23.65591 0.5474444
## 386 64.09357 23.52151 0.5486183
## 387 63.74269 23.38710 0.5494005
## 388 63.50877 23.38710 0.5519237
## 389 63.50877 23.25269 0.5556159
## 390 63.39181 23.25269 0.5573872
## 391 63.39181 22.98387 0.5579881
## 392 63.39181 22.84946 0.5585560
## 393 63.39181 22.71505 0.5591238
## 394 63.27485 22.71505 0.5596914
## 395 63.27485 22.44624 0.5600807
## 396 63.27485 22.17742 0.5604698
## 397 63.15789 22.17742 0.5614259
## 398 62.92398 22.04301 0.5625926
## 399 62.92398 21.90860 0.5635804
## 400 62.92398 21.77419 0.5643574
## 401 62.69006 21.77419 0.5651012
## 402 62.69006 21.63978 0.5660552
## 403 62.69006 21.50538 0.5668311
## 404 62.69006 21.37097 0.5676066
## 405 62.22222 21.37097 0.5683819
## 406 62.22222 21.23656 0.5691567
## 407 62.10526 21.23656 0.5704955
## 408 62.10526 21.10215 0.5718336
## 409 62.10526 20.96774 0.5726069
## 410 61.98830 20.96774 0.5737661
## 411 61.98830 20.83333 0.5772209
## 412 61.87135 20.83333 0.5799193
## 413 61.63743 20.83333 0.5801117
## 414 61.28655 20.69892 0.5804964
## 415 61.16959 20.69892 0.5812655
## 416 61.16959 20.43011 0.5835373
## 417 61.05263 20.43011 0.5852809
## 418 61.05263 20.16129 0.5860474
## 419 60.93567 20.16129 0.5875472
## 420 60.81871 20.16129 0.5892846
## 421 60.81871 20.02688 0.5904314
## 422 60.70175 20.02688 0.5908134
## 423 60.70175 19.89247 0.5914021
## 424 60.58480 19.89247 0.5921332
## 425 60.58480 19.75806 0.5926893
## 426 60.46784 19.75806 0.5938325
## 427 60.35088 19.48925 0.5955300
## 428 60.23392 19.48925 0.5966711
## 429 60.11696 19.48925 0.5972253
## 430 60.11696 19.35484 0.5974312
## 431 60.11696 19.22043 0.5985699
## 432 60.11696 19.08602 0.6000558roc(df$quality,logistic$fitted.values,plot=TRUE, legacy.axes=TRUE, xlab="False Positive Percentage", ylab="True Postive Percentage", col="#377eb8", lwd=4, percent=TRUE)## Setting levels: control = bad, case = good## Setting direction: controls < cases
##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage", ylab = "True Postive Percentage", col = "#377eb8", lwd = 4)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 77.92%roc(df$quality,logistic$fitted.values,plot=TRUE, legacy.axes=TRUE, xlab="False Positive Percentage", ylab="True Postive Percentage", col="#377eb8", lwd=4, percent=TRUE, print.auc=TRUE)## Setting levels: control = bad, case = good## Setting direction: controls < cases
##
## Call:
## roc.default(response = df$quality, predictor = logistic$fitted.values, percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage", ylab = "True Postive Percentage", col = "#377eb8", lwd = 4, print.auc = TRUE)
##
## Data: logistic$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 77.92%`
# Lets do two roc plots to understand which model is better
roc(df$quality, logistic_simple$fitted.values, plot=TRUE, legacy.axes=TRUE, percent=TRUE, xlab="False Positive Percentage", ylab="True Postive Percentage", col="#377eb8", lwd=4, print.auc=TRUE)## Setting levels: control = bad, case = good## Setting direction: controls < cases##
## Call:
## roc.default(response = df$quality, predictor = logistic_simple$fitted.values, percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage", ylab = "True Postive Percentage", col = "#377eb8", lwd = 4, print.auc = TRUE)
##
## Data: logistic_simple$fitted.values in 744 controls (df$quality bad) < 855 cases (df$quality good).
## Area under the curve: 79.55%# Lets add the other graph
plot.roc(df$quality, logistic$fitted.values, percent=TRUE, col="#4daf4a", lwd=4, print.auc=TRUE, add=TRUE, print.auc.y=40)## Setting levels: control = bad, case = good
## Setting direction: controls < caseslegend("bottomright", legend=c("Simple", "Non Simple"), col=c("#377eb8", "#4daf4a"), lwd=4) 
library(readr)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(ggplot2)
library(memisc)## Warning: package 'memisc' was built under R version 4.3.3##
## Attaching package: 'memisc'## The following object is masked from 'package:magrittr':
##
## %$%## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:lubridate':
##
## as.interval, is.interval## The following object is masked from 'package:purrr':
##
## %@%## The following object is masked from 'package:tibble':
##
## view## The following objects are masked from 'package:dplyr':
##
## collect, recode, rename, syms## The following object is masked from 'package:ggplot2':
##
## syms## The following objects are masked from 'package:stats':
##
## contr.sum, contr.treatment, contrasts## The following object is masked from 'package:base':
##
## as.arraylibrary(ROCR)## Warning: package 'ROCR' was built under R version 4.3.3library(dplyr)
library(klaR)## Warning: package 'klaR' was built under R version 4.3.3# Read the CSV file
df <- read_csv("C:/Rutgers/Subjects/Spring Sem/Multivariate Analysis/Data/wine.csv")## Rows: 1599 Columns: 12## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): quality
## dbl (11): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#dataframe
df## # A tibble: 1,599 × 12
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## 2 7.8 0.88 0 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.7 0 1.9 0.076
## 6 7.4 0.66 0 1.8 0.075
## 7 7.9 0.6 0.06 1.6 0.069
## 8 7.3 0.65 0 1.2 0.065
## 9 7.8 0.58 0.02 2 0.073
## 10 7.5 0.5 0.36 6.1 0.071
## # ℹ 1,589 more rows
## # ℹ 7 more variables: `free sulfur dioxide` <dbl>,
## # `total sulfur dioxide` <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <chr>df1 <- as.matrix(df[,c(1:11)])
df1## fixed acidity volatile acidity citric acid residual sugar chlorides
## [1,] 7.4 0.700 0.00 1.90 0.076
## [2,] 7.8 0.880 0.00 2.60 0.098
## [3,] 7.8 0.760 0.04 2.30 0.092
## [4,] 11.2 0.280 0.56 1.90 0.075
## [5,] 7.4 0.700 0.00 1.90 0.076
## [6,] 7.4 0.660 0.00 1.80 0.075
## [7,] 7.9 0.600 0.06 1.60 0.069
## [8,] 7.3 0.650 0.00 1.20 0.065
## [9,] 7.8 0.580 0.02 2.00 0.073
## [10,] 7.5 0.500 0.36 6.10 0.071
## [11,] 6.7 0.580 0.08 1.80 0.097
## [12,] 7.5 0.500 0.36 6.10 0.071
## [13,] 5.6 0.615 0.00 1.60 0.089
## [14,] 7.8 0.610 0.29 1.60 0.114
## [15,] 8.9 0.620 0.18 3.80 0.176
## [16,] 8.9 0.620 0.19 3.90 0.170
## [17,] 8.5 0.280 0.56 1.80 0.092
## [18,] 8.1 0.560 0.28 1.70 0.368
## [19,] 7.4 0.590 0.08 4.40 0.086
## [20,] 7.9 0.320 0.51 1.80 0.341
## [21,] 8.9 0.220 0.48 1.80 0.077
## [22,] 7.6 0.390 0.31 2.30 0.082
## [23,] 7.9 0.430 0.21 1.60 0.106
## [24,] 8.5 0.490 0.11 2.30 0.084
## [25,] 6.9 0.400 0.14 2.40 0.085
## [26,] 6.3 0.390 0.16 1.40 0.080
## [27,] 7.6 0.410 0.24 1.80 0.080
## [28,] 7.9 0.430 0.21 1.60 0.106
## [29,] 7.1 0.710 0.00 1.90 0.080
## [30,] 7.8 0.645 0.00 2.00 0.082
## [31,] 6.7 0.675 0.07 2.40 0.089
## [32,] 6.9 0.685 0.00 2.50 0.105
## [33,] 8.3 0.655 0.12 2.30 0.083
## [34,] 6.9 0.605 0.12 10.70 0.073
## [35,] 5.2 0.320 0.25 1.80 0.103
## [36,] 7.8 0.645 0.00 5.50 0.086
## [37,] 7.8 0.600 0.14 2.40 0.086
## [38,] 8.1 0.380 0.28 2.10 0.066
## [39,] 5.7 1.130 0.09 1.50 0.172
## [40,] 7.3 0.450 0.36 5.90 0.074
## [41,] 7.3 0.450 0.36 5.90 0.074
## [42,] 8.8 0.610 0.30 2.80 0.088
## [43,] 7.5 0.490 0.20 2.60 0.332
## [44,] 8.1 0.660 0.22 2.20 0.069
## [45,] 6.8 0.670 0.02 1.80 0.050
## [46,] 4.6 0.520 0.15 2.10 0.054
## [47,] 7.7 0.935 0.43 2.20 0.114
## [48,] 8.7 0.290 0.52 1.60 0.113
## [49,] 6.4 0.400 0.23 1.60 0.066
## [50,] 5.6 0.310 0.37 1.40 0.074
## [51,] 8.8 0.660 0.26 1.70 0.074
## [52,] 6.6 0.520 0.04 2.20 0.069
## [53,] 6.6 0.500 0.04 2.10 0.068
## [54,] 8.6 0.380 0.36 3.00 0.081
## [55,] 7.6 0.510 0.15 2.80 0.110
## [56,] 7.7 0.620 0.04 3.80 0.084
## [57,] 10.2 0.420 0.57 3.40 0.070
## [58,] 7.5 0.630 0.12 5.10 0.111
## [59,] 7.8 0.590 0.18 2.30 0.076
## [60,] 7.3 0.390 0.31 2.40 0.074
## [61,] 8.8 0.400 0.40 2.20 0.079
## [62,] 7.7 0.690 0.49 1.80 0.115
## [63,] 7.5 0.520 0.16 1.90 0.085
## [64,] 7.0 0.735 0.05 2.00 0.081
## [65,] 7.2 0.725 0.05 4.65 0.086
## [66,] 7.2 0.725 0.05 4.65 0.086
## [67,] 7.5 0.520 0.11 1.50 0.079
## [68,] 6.6 0.705 0.07 1.60 0.076
## [69,] 9.3 0.320 0.57 2.00 0.074
## [70,] 8.0 0.705 0.05 1.90 0.074
## [71,] 7.7 0.630 0.08 1.90 0.076
## [72,] 7.7 0.670 0.23 2.10 0.088
## [73,] 7.7 0.690 0.22 1.90 0.084
## [74,] 8.3 0.675 0.26 2.10 0.084
## [75,] 9.7 0.320 0.54 2.50 0.094
## [76,] 8.8 0.410 0.64 2.20 0.093
## [77,] 8.8 0.410 0.64 2.20 0.093
## [78,] 6.8 0.785 0.00 2.40 0.104
## [79,] 6.7 0.750 0.12 2.00 0.086
## [80,] 8.3 0.625 0.20 1.50 0.080
## [81,] 6.2 0.450 0.20 1.60 0.069
## [82,] 7.8 0.430 0.70 1.90 0.464
## [83,] 7.4 0.500 0.47 2.00 0.086
## [84,] 7.3 0.670 0.26 1.80 0.401
## [85,] 6.3 0.300 0.48 1.80 0.069
## [86,] 6.9 0.550 0.15 2.20 0.076
## [87,] 8.6 0.490 0.28 1.90 0.110
## [88,] 7.7 0.490 0.26 1.90 0.062
## [89,] 9.3 0.390 0.44 2.10 0.107
## [90,] 7.0 0.620 0.08 1.80 0.076
## [91,] 7.9 0.520 0.26 1.90 0.079
## [92,] 8.6 0.490 0.28 1.90 0.110
## [93,] 8.6 0.490 0.29 2.00 0.110
## [94,] 7.7 0.490 0.26 1.90 0.062
## [95,] 5.0 1.020 0.04 1.40 0.045
## [96,] 4.7 0.600 0.17 2.30 0.058
## [97,] 6.8 0.775 0.00 3.00 0.102
## [98,] 7.0 0.500 0.25 2.00 0.070
## [99,] 7.6 0.900 0.06 2.50 0.079
## [100,] 8.1 0.545 0.18 1.90 0.080
## [101,] 8.3 0.610 0.30 2.10 0.084
## [102,] 7.8 0.500 0.30 1.90 0.075
## [103,] 8.1 0.545 0.18 1.90 0.080
## [104,] 8.1 0.575 0.22 2.10 0.077
## [105,] 7.2 0.490 0.24 2.20 0.070
## [106,] 8.1 0.575 0.22 2.10 0.077
## [107,] 7.8 0.410 0.68 1.70 0.467
## [108,] 6.2 0.630 0.31 1.70 0.088
## [109,] 8.0 0.330 0.53 2.50 0.091
## [110,] 8.1 0.785 0.52 2.00 0.122
## [111,] 7.8 0.560 0.19 1.80 0.104
## [112,] 8.4 0.620 0.09 2.20 0.084
## [113,] 8.4 0.600 0.10 2.20 0.085
## [114,] 10.1 0.310 0.44 2.30 0.080
## [115,] 7.8 0.560 0.19 1.80 0.104
## [116,] 9.4 0.400 0.31 2.20 0.090
## [117,] 8.3 0.540 0.28 1.90 0.077
## [118,] 7.8 0.560 0.12 2.00 0.082
## [119,] 8.8 0.550 0.04 2.20 0.119
## [120,] 7.0 0.690 0.08 1.80 0.097
## [121,] 7.3 1.070 0.09 1.70 0.178
## [122,] 8.8 0.550 0.04 2.20 0.119
## [123,] 7.3 0.695 0.00 2.50 0.075
## [124,] 8.0 0.710 0.00 2.60 0.080
## [125,] 7.8 0.500 0.17 1.60 0.082
## [126,] 9.0 0.620 0.04 1.90 0.146
## [127,] 8.2 1.330 0.00 1.70 0.081
## [128,] 8.1 1.330 0.00 1.80 0.082
## [129,] 8.0 0.590 0.16 1.80 0.065
## [130,] 6.1 0.380 0.15 1.80 0.072
## [131,] 8.0 0.745 0.56 2.00 0.118
## [132,] 5.6 0.500 0.09 2.30 0.049
## [133,] 5.6 0.500 0.09 2.30 0.049
## [134,] 6.6 0.500 0.01 1.50 0.060
## [135,] 7.9 1.040 0.05 2.20 0.084
## [136,] 8.4 0.745 0.11 1.90 0.090
## [137,] 8.3 0.715 0.15 1.80 0.089
## [138,] 7.2 0.415 0.36 2.00 0.081
## [139,] 7.8 0.560 0.19 2.10 0.081
## [140,] 7.8 0.560 0.19 2.00 0.081
## [141,] 8.4 0.745 0.11 1.90 0.090
## [142,] 8.3 0.715 0.15 1.80 0.089
## [143,] 5.2 0.340 0.00 1.80 0.050
## [144,] 6.3 0.390 0.08 1.70 0.066
## [145,] 5.2 0.340 0.00 1.80 0.050
## [146,] 8.1 0.670 0.55 1.80 0.117
## [147,] 5.8 0.680 0.02 1.80 0.087
## [148,] 7.6 0.490 0.26 1.60 0.236
## [149,] 6.9 0.490 0.10 2.30 0.074
## [150,] 8.2 0.400 0.44 2.80 0.089
## [151,] 7.3 0.330 0.47 2.10 0.077
## [152,] 9.2 0.520 1.00 3.40 0.610
## [153,] 7.5 0.600 0.03 1.80 0.095
## [154,] 7.5 0.600 0.03 1.80 0.095
## [155,] 7.1 0.430 0.42 5.50 0.070
## [156,] 7.1 0.430 0.42 5.50 0.071
## [157,] 7.1 0.430 0.42 5.50 0.070
## [158,] 7.1 0.430 0.42 5.50 0.071
## [159,] 7.1 0.680 0.00 2.20 0.073
## [160,] 6.8 0.600 0.18 1.90 0.079
## [161,] 7.6 0.950 0.03 2.00 0.090
## [162,] 7.6 0.680 0.02 1.30 0.072
## [163,] 7.8 0.530 0.04 1.70 0.076
## [164,] 7.4 0.600 0.26 7.30 0.070
## [165,] 7.3 0.590 0.26 7.20 0.070
## [166,] 7.8 0.630 0.48 1.70 0.100
## [167,] 6.8 0.640 0.10 2.10 0.085
## [168,] 7.3 0.550 0.03 1.60 0.072
## [169,] 6.8 0.630 0.07 2.10 0.089
## [170,] 7.5 0.705 0.24 1.80 0.360
## [171,] 7.9 0.885 0.03 1.80 0.058
## [172,] 8.0 0.420 0.17 2.00 0.073
## [173,] 8.0 0.420 0.17 2.00 0.073
## [174,] 7.4 0.620 0.05 1.90 0.068
## [175,] 7.3 0.380 0.21 2.00 0.080
## [176,] 6.9 0.500 0.04 1.50 0.085
## [177,] 7.3 0.380 0.21 2.00 0.080
## [178,] 7.5 0.520 0.42 2.30 0.087
## [179,] 7.0 0.805 0.00 2.50 0.068
## [180,] 8.8 0.610 0.14 2.40 0.067
## [181,] 8.8 0.610 0.14 2.40 0.067
## [182,] 8.9 0.610 0.49 2.00 0.270
## [183,] 7.2 0.730 0.02 2.50 0.076
## [184,] 6.8 0.610 0.20 1.80 0.077
## [185,] 6.7 0.620 0.21 1.90 0.079
## [186,] 8.9 0.310 0.57 2.00 0.111
## [187,] 7.4 0.390 0.48 2.00 0.082
## [188,] 7.7 0.705 0.10 2.60 0.084
## [189,] 7.9 0.500 0.33 2.00 0.084
## [190,] 7.9 0.490 0.32 1.90 0.082
## [191,] 8.2 0.500 0.35 2.90 0.077
## [192,] 6.4 0.370 0.25 1.90 0.074
## [193,] 6.8 0.630 0.12 3.80 0.099
## [194,] 7.6 0.550 0.21 2.20 0.071
## [195,] 7.6 0.550 0.21 2.20 0.071
## [196,] 7.8 0.590 0.33 2.00 0.074
## [197,] 7.3 0.580 0.30 2.40 0.074
## [198,] 11.5 0.300 0.60 2.00 0.067
## [199,] 5.4 0.835 0.08 1.20 0.046
## [200,] 6.9 1.090 0.06 2.10 0.061
## [201,] 9.6 0.320 0.47 1.40 0.056
## [202,] 8.8 0.370 0.48 2.10 0.097
## [203,] 6.8 0.500 0.11 1.50 0.075
## [204,] 7.0 0.420 0.35 1.60 0.088
## [205,] 7.0 0.430 0.36 1.60 0.089
## [206,] 12.8 0.300 0.74 2.60 0.095
## [207,] 12.8 0.300 0.74 2.60 0.095
## [208,] 7.8 0.570 0.31 1.80 0.069
## [209,] 7.8 0.440 0.28 2.70 0.100
## [210,] 11.0 0.300 0.58 2.10 0.054
## [211,] 9.7 0.530 0.60 2.00 0.039
## [212,] 8.0 0.725 0.24 2.80 0.083
## [213,] 11.6 0.440 0.64 2.10 0.059
## [214,] 8.2 0.570 0.26 2.20 0.060
## [215,] 7.8 0.735 0.08 2.40 0.092
## [216,] 7.0 0.490 0.49 5.60 0.060
## [217,] 8.7 0.625 0.16 2.00 0.101
## [218,] 8.1 0.725 0.22 2.20 0.072
## [219,] 7.5 0.490 0.19 1.90 0.076
## [220,] 7.8 0.530 0.33 2.40 0.080
## [221,] 7.8 0.340 0.37 2.00 0.082
## [222,] 7.4 0.530 0.26 2.00 0.101
## [223,] 6.8 0.610 0.04 1.50 0.057
## [224,] 8.6 0.645 0.25 2.00 0.083
## [225,] 8.4 0.635 0.36 2.00 0.089
## [226,] 7.7 0.430 0.25 2.60 0.073
## [227,] 8.9 0.590 0.50 2.00 0.337
## [228,] 9.0 0.820 0.14 2.60 0.089
## [229,] 7.7 0.430 0.25 2.60 0.073
## [230,] 6.9 0.520 0.25 2.60 0.081
## [231,] 5.2 0.480 0.04 1.60 0.054
## [232,] 8.0 0.380 0.06 1.80 0.078
## [233,] 8.5 0.370 0.20 2.80 0.090
## [234,] 6.9 0.520 0.25 2.60 0.081
## [235,] 8.2 1.000 0.09 2.30 0.065
## [236,] 7.2 0.630 0.00 1.90 0.097
## [237,] 7.2 0.630 0.00 1.90 0.097
## [238,] 7.2 0.645 0.00 1.90 0.097
## [239,] 7.2 0.630 0.00 1.90 0.097
## [240,] 8.2 1.000 0.09 2.30 0.065
## [241,] 8.9 0.635 0.37 1.70 0.263
## [242,] 12.0 0.380 0.56 2.10 0.093
## [243,] 7.7 0.580 0.10 1.80 0.102
## [244,] 15.0 0.210 0.44 2.20 0.075
## [245,] 15.0 0.210 0.44 2.20 0.075
## [246,] 7.3 0.660 0.00 2.00 0.084
## [247,] 7.1 0.680 0.07 1.90 0.075
## [248,] 8.2 0.600 0.17 2.30 0.072
## [249,] 7.7 0.530 0.06 1.70 0.074
## [250,] 7.3 0.660 0.00 2.00 0.084
## [251,] 10.8 0.320 0.44 1.60 0.063
## [252,] 7.1 0.600 0.00 1.80 0.074
## [253,] 11.1 0.350 0.48 3.10 0.090
## [254,] 7.7 0.775 0.42 1.90 0.092
## [255,] 7.1 0.600 0.00 1.80 0.074
## [256,] 8.0 0.570 0.23 3.20 0.073
## [257,] 9.4 0.340 0.37 2.20 0.075
## [258,] 6.6 0.695 0.00 2.10 0.075
## [259,] 7.7 0.410 0.76 1.80 0.611
## [260,] 10.0 0.310 0.47 2.60 0.085
## [261,] 7.9 0.330 0.23 1.70 0.077
## [262,] 7.0 0.975 0.04 2.00 0.087
## [263,] 8.0 0.520 0.03 1.70 0.070
## [264,] 7.9 0.370 0.23 1.80 0.077
## [265,] 12.5 0.560 0.49 2.40 0.064
## [266,] 11.8 0.260 0.52 1.80 0.071
## [267,] 8.1 0.870 0.00 3.30 0.096
## [268,] 7.9 0.350 0.46 3.60 0.078
## [269,] 6.9 0.540 0.04 3.00 0.077
## [270,] 11.5 0.180 0.51 4.00 0.104
## [271,] 7.9 0.545 0.06 4.00 0.087
## [272,] 11.5 0.180 0.51 4.00 0.104
## [273,] 10.9 0.370 0.58 4.00 0.071
## [274,] 8.4 0.715 0.20 2.40 0.076
## [275,] 7.5 0.650 0.18 7.00 0.088
## [276,] 7.9 0.545 0.06 4.00 0.087
## [277,] 6.9 0.540 0.04 3.00 0.077
## [278,] 11.5 0.180 0.51 4.00 0.104
## [279,] 10.3 0.320 0.45 6.40 0.073
## [280,] 8.9 0.400 0.32 5.60 0.087
## [281,] 11.4 0.260 0.44 3.60 0.071
## [282,] 7.7 0.270 0.68 3.50 0.358
## [283,] 7.6 0.520 0.12 3.00 0.067
## [284,] 8.9 0.400 0.32 5.60 0.087
## [285,] 9.9 0.590 0.07 3.40 0.102
## [286,] 9.9 0.590 0.07 3.40 0.102
## [287,] 12.0 0.450 0.55 2.00 0.073
## [288,] 7.5 0.400 0.12 3.00 0.092
## [289,] 8.7 0.520 0.09 2.50 0.091
## [290,] 11.6 0.420 0.53 3.30 0.105
## [291,] 8.7 0.520 0.09 2.50 0.091
## [292,] 11.0 0.200 0.48 2.00 0.343
## [293,] 10.4 0.550 0.23 2.70 0.091
## [294,] 6.9 0.360 0.25 2.40 0.098
## [295,] 13.3 0.340 0.52 3.20 0.094
## [296,] 10.8 0.500 0.46 2.50 0.073
## [297,] 10.6 0.830 0.37 2.60 0.086
## [298,] 7.1 0.630 0.06 2.00 0.083
## [299,] 7.2 0.650 0.02 2.30 0.094
## [300,] 6.9 0.670 0.06 2.10 0.080
## [301,] 7.5 0.530 0.06 2.60 0.086
## [302,] 11.1 0.180 0.48 1.50 0.068
## [303,] 8.3 0.705 0.12 2.60 0.092
## [304,] 7.4 0.670 0.12 1.60 0.186
## [305,] 8.4 0.650 0.60 2.10 0.112
## [306,] 10.3 0.530 0.48 2.50 0.063
## [307,] 7.6 0.620 0.32 2.20 0.082
## [308,] 10.3 0.410 0.42 2.40 0.213
## [309,] 10.3 0.430 0.44 2.40 0.214
## [310,] 7.4 0.290 0.38 1.70 0.062
## [311,] 10.3 0.530 0.48 2.50 0.063
## [312,] 7.9 0.530 0.24 2.00 0.072
## [313,] 9.0 0.460 0.31 2.80 0.093
## [314,] 8.6 0.470 0.30 3.00 0.076
## [315,] 7.4 0.360 0.29 2.60 0.087
## [316,] 7.1 0.350 0.29 2.50 0.096
## [317,] 9.6 0.560 0.23 3.40 0.102
## [318,] 9.6 0.770 0.12 2.90 0.082
## [319,] 9.8 0.660 0.39 3.20 0.083
## [320,] 9.6 0.770 0.12 2.90 0.082
## [321,] 9.8 0.660 0.39 3.20 0.083
## [322,] 9.3 0.610 0.26 3.40 0.090
## [323,] 7.8 0.620 0.05 2.30 0.079
## [324,] 10.3 0.590 0.42 2.80 0.090
## [325,] 10.0 0.490 0.20 11.00 0.071
## [326,] 10.0 0.490 0.20 11.00 0.071
## [327,] 11.6 0.530 0.66 3.65 0.121
## [328,] 10.3 0.440 0.50 4.50 0.107
## [329,] 13.4 0.270 0.62 2.60 0.082
## [330,] 10.7 0.460 0.39 2.00 0.061
## [331,] 10.2 0.360 0.64 2.90 0.122
## [332,] 10.2 0.360 0.64 2.90 0.122
## [333,] 8.0 0.580 0.28 3.20 0.066
## [334,] 8.4 0.560 0.08 2.10 0.105
## [335,] 7.9 0.650 0.01 2.50 0.078
## [336,] 11.9 0.695 0.53 3.40 0.128
## [337,] 8.9 0.430 0.45 1.90 0.052
## [338,] 7.8 0.430 0.32 2.80 0.080
## [339,] 12.4 0.490 0.58 3.00 0.103
## [340,] 12.5 0.280 0.54 2.30 0.082
## [341,] 12.2 0.340 0.50 2.40 0.066
## [342,] 10.6 0.420 0.48 2.70 0.065
## [343,] 10.9 0.390 0.47 1.80 0.118
## [344,] 10.9 0.390 0.47 1.80 0.118
## [345,] 11.9 0.570 0.50 2.60 0.082
## [346,] 7.0 0.685 0.00 1.90 0.067
## [347,] 6.6 0.815 0.02 2.70 0.072
## [348,] 13.8 0.490 0.67 3.00 0.093
## [349,] 9.6 0.560 0.31 2.80 0.089
## [350,] 9.1 0.785 0.00 2.60 0.093
## [351,] 10.7 0.670 0.22 2.70 0.107
## [352,] 9.1 0.795 0.00 2.60 0.096
## [353,] 7.7 0.665 0.00 2.40 0.090
## [354,] 13.5 0.530 0.79 4.80 0.120
## [355,] 6.1 0.210 0.40 1.40 0.066
## [356,] 6.7 0.750 0.01 2.40 0.078
## [357,] 11.5 0.410 0.52 3.00 0.080
## [358,] 10.5 0.420 0.66 2.95 0.116
## [359,] 11.9 0.430 0.66 3.10 0.109
## [360,] 12.6 0.380 0.66 2.60 0.088
## [361,] 8.2 0.700 0.23 2.00 0.099
## [362,] 8.6 0.450 0.31 2.60 0.086
## [363,] 11.9 0.580 0.66 2.50 0.072
## [364,] 12.5 0.460 0.63 2.00 0.071
## [365,] 12.8 0.615 0.66 5.80 0.083
## [366,] 10.0 0.420 0.50 3.40 0.107
## [367,] 12.8 0.615 0.66 5.80 0.083
## [368,] 10.4 0.575 0.61 2.60 0.076
## [369,] 10.3 0.340 0.52 2.80 0.159
## [370,] 9.4 0.270 0.53 2.40 0.074
## [371,] 6.9 0.765 0.02 2.30 0.063
## [372,] 7.9 0.240 0.40 1.60 0.056
## [373,] 9.1 0.280 0.48 1.80 0.067
## [374,] 7.4 0.550 0.22 2.20 0.106
## [375,] 14.0 0.410 0.63 3.80 0.089
## [376,] 11.5 0.540 0.71 4.40 0.124
## [377,] 11.5 0.450 0.50 3.00 0.078
## [378,] 9.4 0.270 0.53 2.40 0.074
## [379,] 11.4 0.625 0.66 6.20 0.088
## [380,] 8.3 0.420 0.38 2.50 0.094
## [381,] 8.3 0.260 0.42 2.00 0.080
## [382,] 13.7 0.415 0.68 2.90 0.085
## [383,] 8.3 0.260 0.42 2.00 0.080
## [384,] 8.3 0.260 0.42 2.00 0.080
## [385,] 7.7 0.510 0.28 2.10 0.087
## [386,] 7.4 0.630 0.07 2.40 0.090
## [387,] 7.8 0.540 0.26 2.00 0.088
## [388,] 8.3 0.660 0.15 1.90 0.079
## [389,] 7.8 0.460 0.26 1.90 0.088
## [390,] 9.6 0.380 0.31 2.50 0.096
## [391,] 5.6 0.850 0.05 1.40 0.045
## [392,] 13.7 0.415 0.68 2.90 0.085
## [393,] 9.5 0.370 0.52 2.00 0.082
## [394,] 8.4 0.665 0.61 2.00 0.112
## [395,] 12.7 0.600 0.65 2.30 0.063
## [396,] 12.0 0.370 0.76 4.20 0.066
## [397,] 6.6 0.735 0.02 7.90 0.122
## [398,] 11.5 0.590 0.59 2.60 0.087
## [399,] 11.5 0.590 0.59 2.60 0.087
## [400,] 8.7 0.765 0.22 2.30 0.064
## [401,] 6.6 0.735 0.02 7.90 0.122
## [402,] 7.7 0.260 0.30 1.70 0.059
## [403,] 12.2 0.480 0.54 2.60 0.085
## [404,] 11.4 0.600 0.49 2.70 0.085
## [405,] 7.7 0.690 0.05 2.70 0.075
## [406,] 8.7 0.310 0.46 1.40 0.059
## [407,] 9.8 0.440 0.47 2.50 0.063
## [408,] 12.0 0.390 0.66 3.00 0.093
## [409,] 10.4 0.340 0.58 3.70 0.174
## [410,] 12.5 0.460 0.49 4.50 0.070
## [411,] 9.0 0.430 0.34 2.50 0.080
## [412,] 9.1 0.450 0.35 2.40 0.080
## [413,] 7.1 0.735 0.16 1.90 0.100
## [414,] 9.9 0.400 0.53 6.70 0.097
## [415,] 8.8 0.520 0.34 2.70 0.087
## [416,] 8.6 0.725 0.24 6.60 0.117
## [417,] 10.6 0.480 0.64 2.20 0.111
## [418,] 7.0 0.580 0.12 1.90 0.091
## [419,] 11.9 0.380 0.51 2.00 0.121
## [420,] 6.8 0.770 0.00 1.80 0.066
## [421,] 9.5 0.560 0.33 2.40 0.089
## [422,] 6.6 0.840 0.03 2.30 0.059
## [423,] 7.7 0.960 0.20 2.00 0.047
## [424,] 10.5 0.240 0.47 2.10 0.066
## [425,] 7.7 0.960 0.20 2.00 0.047
## [426,] 6.6 0.840 0.03 2.30 0.059
## [427,] 6.4 0.670 0.08 2.10 0.045
## [428,] 9.5 0.780 0.22 1.90 0.077
## [429,] 9.1 0.520 0.33 1.30 0.070
## [430,] 12.8 0.840 0.63 2.40 0.088
## [431,] 10.5 0.240 0.47 2.10 0.066
## [432,] 7.8 0.550 0.35 2.20 0.074
## [433,] 11.9 0.370 0.69 2.30 0.078
## [434,] 12.3 0.390 0.63 2.30 0.091
## [435,] 10.4 0.410 0.55 3.20 0.076
## [436,] 12.3 0.390 0.63 2.30 0.091
## [437,] 8.0 0.670 0.30 2.00 0.060
## [438,] 11.1 0.450 0.73 3.20 0.066
## [439,] 10.4 0.410 0.55 3.20 0.076
## [440,] 7.0 0.620 0.18 1.50 0.062
## [441,] 12.6 0.310 0.72 2.20 0.072
## [442,] 11.9 0.400 0.65 2.15 0.068
## [443,] 15.6 0.685 0.76 3.70 0.100
## [444,] 10.0 0.440 0.49 2.70 0.077
## [445,] 5.3 0.570 0.01 1.70 0.054
## [446,] 9.5 0.735 0.10 2.10 0.079
## [447,] 12.5 0.380 0.60 2.60 0.081
## [448,] 9.3 0.480 0.29 2.10 0.127
## [449,] 8.6 0.530 0.22 2.00 0.100
## [450,] 11.9 0.390 0.69 2.80 0.095
## [451,] 11.9 0.390 0.69 2.80 0.095
## [452,] 8.4 0.370 0.53 1.80 0.413
## [453,] 6.8 0.560 0.03 1.70 0.084
## [454,] 10.4 0.330 0.63 2.80 0.084
## [455,] 7.0 0.230 0.40 1.60 0.063
## [456,] 11.3 0.620 0.67 5.20 0.086
## [457,] 8.9 0.590 0.39 2.30 0.095
## [458,] 9.2 0.630 0.21 2.70 0.097
## [459,] 10.4 0.330 0.63 2.80 0.084
## [460,] 11.6 0.580 0.66 2.20 0.074
## [461,] 9.2 0.430 0.52 2.30 0.083
## [462,] 8.3 0.615 0.22 2.60 0.087
## [463,] 11.0 0.260 0.68 2.55 0.085
## [464,] 8.1 0.660 0.70 2.20 0.098
## [465,] 11.5 0.315 0.54 2.10 0.084
## [466,] 10.0 0.290 0.40 2.90 0.098
## [467,] 10.3 0.500 0.42 2.00 0.069
## [468,] 8.8 0.460 0.45 2.60 0.065
## [469,] 11.4 0.360 0.69 2.10 0.090
## [470,] 8.7 0.820 0.02 1.20 0.070
## [471,] 13.0 0.320 0.65 2.60 0.093
## [472,] 9.6 0.540 0.42 2.40 0.081
## [473,] 12.5 0.370 0.55 2.60 0.083
## [474,] 9.9 0.350 0.55 2.10 0.062
## [475,] 10.5 0.280 0.51 1.70 0.080
## [476,] 9.6 0.680 0.24 2.20 0.087
## [477,] 9.3 0.270 0.41 2.00 0.091
## [478,] 10.4 0.240 0.49 1.80 0.075
## [479,] 9.6 0.680 0.24 2.20 0.087
## [480,] 9.4 0.685 0.11 2.70 0.077
## [481,] 10.6 0.280 0.39 15.50 0.069
## [482,] 9.4 0.300 0.56 2.80 0.080
## [483,] 10.6 0.360 0.59 2.20 0.152
## [484,] 10.6 0.360 0.60 2.20 0.152
## [485,] 10.6 0.440 0.68 4.10 0.114
## [486,] 10.2 0.670 0.39 1.90 0.054
## [487,] 10.2 0.670 0.39 1.90 0.054
## [488,] 10.2 0.645 0.36 1.80 0.053
## [489,] 11.6 0.320 0.55 2.80 0.081
## [490,] 9.3 0.390 0.40 2.60 0.073
## [491,] 9.3 0.775 0.27 2.80 0.078
## [492,] 9.2 0.410 0.50 2.50 0.055
## [493,] 8.9 0.400 0.51 2.60 0.052
## [494,] 8.7 0.690 0.31 3.00 0.086
## [495,] 6.5 0.390 0.23 8.30 0.051
## [496,] 10.7 0.350 0.53 2.60 0.070
## [497,] 7.8 0.520 0.25 1.90 0.081
## [498,] 7.2 0.340 0.32 2.50 0.090
## [499,] 10.7 0.350 0.53 2.60 0.070
## [500,] 8.7 0.690 0.31 3.00 0.086
## [501,] 7.8 0.520 0.25 1.90 0.081
## [502,] 10.4 0.440 0.73 6.55 0.074
## [503,] 10.4 0.440 0.73 6.55 0.074
## [504,] 10.5 0.260 0.47 1.90 0.078
## [505,] 10.5 0.240 0.42 1.80 0.077
## [506,] 10.2 0.490 0.63 2.90 0.072
## [507,] 10.4 0.240 0.46 1.80 0.075
## [508,] 11.2 0.670 0.55 2.30 0.084
## [509,] 10.0 0.590 0.31 2.20 0.090
## [510,] 13.3 0.290 0.75 2.80 0.084
## [511,] 12.4 0.420 0.49 4.60 0.073
## [512,] 10.0 0.590 0.31 2.20 0.090
## [513,] 10.7 0.400 0.48 2.10 0.125
## [514,] 10.5 0.510 0.64 2.40 0.107
## [515,] 10.5 0.510 0.64 2.40 0.107
## [516,] 8.5 0.655 0.49 6.10 0.122
## [517,] 12.5 0.600 0.49 4.30 0.100
## [518,] 10.4 0.610 0.49 2.10 0.200
## [519,] 10.9 0.210 0.49 2.80 0.088
## [520,] 7.3 0.365 0.49 2.50 0.088
## [521,] 9.8 0.250 0.49 2.70 0.088
## [522,] 7.6 0.410 0.49 2.00 0.088
## [523,] 8.2 0.390 0.49 2.30 0.099
## [524,] 9.3 0.400 0.49 2.50 0.085
## [525,] 9.2 0.430 0.49 2.40 0.086
## [526,] 10.4 0.640 0.24 2.80 0.105
## [527,] 7.3 0.365 0.49 2.50 0.088
## [528,] 7.0 0.380 0.49 2.50 0.097
## [529,] 8.2 0.420 0.49 2.60 0.084
## [530,] 9.9 0.630 0.24 2.40 0.077
## [531,] 9.1 0.220 0.24 2.10 0.078
## [532,] 11.9 0.380 0.49 2.70 0.098
## [533,] 11.9 0.380 0.49 2.70 0.098
## [534,] 10.3 0.270 0.24 2.10 0.072
## [535,] 10.0 0.480 0.24 2.70 0.102
## [536,] 9.1 0.220 0.24 2.10 0.078
## [537,] 9.9 0.630 0.24 2.40 0.077
## [538,] 8.1 0.825 0.24 2.10 0.084
## [539,] 12.9 0.350 0.49 5.80 0.066
## [540,] 11.2 0.500 0.74 5.15 0.100
## [541,] 9.2 0.590 0.24 3.30 0.101
## [542,] 9.5 0.460 0.49 6.30 0.064
## [543,] 9.3 0.715 0.24 2.10 0.070
## [544,] 11.2 0.660 0.24 2.50 0.085
## [545,] 14.3 0.310 0.74 1.80 0.075
## [546,] 9.1 0.470 0.49 2.60 0.094
## [547,] 7.5 0.550 0.24 2.00 0.078
## [548,] 10.6 0.310 0.49 2.50 0.067
## [549,] 12.4 0.350 0.49 2.60 0.079
## [550,] 9.0 0.530 0.49 1.90 0.171
## [551,] 6.8 0.510 0.01 2.10 0.074
## [552,] 9.4 0.430 0.24 2.80 0.092
## [553,] 9.5 0.460 0.24 2.70 0.092
## [554,] 5.0 1.040 0.24 1.60 0.050
## [555,] 15.5 0.645 0.49 4.20 0.095
## [556,] 15.5 0.645 0.49 4.20 0.095
## [557,] 10.9 0.530 0.49 4.60 0.118
## [558,] 15.6 0.645 0.49 4.20 0.095
## [559,] 10.9 0.530 0.49 4.60 0.118
## [560,] 13.0 0.470 0.49 4.30 0.085
## [561,] 12.7 0.600 0.49 2.80 0.075
## [562,] 9.0 0.440 0.49 2.40 0.078
## [563,] 9.0 0.540 0.49 2.90 0.094
## [564,] 7.6 0.290 0.49 2.70 0.092
## [565,] 13.0 0.470 0.49 4.30 0.085
## [566,] 12.7 0.600 0.49 2.80 0.075
## [567,] 8.7 0.700 0.24 2.50 0.226
## [568,] 8.7 0.700 0.24 2.50 0.226
## [569,] 9.8 0.500 0.49 2.60 0.250
## [570,] 6.2 0.360 0.24 2.20 0.095
## [571,] 11.5 0.350 0.49 3.30 0.070
## [572,] 6.2 0.360 0.24 2.20 0.095
## [573,] 10.2 0.240 0.49 2.40 0.075
## [574,] 10.5 0.590 0.49 2.10 0.070
## [575,] 10.6 0.340 0.49 3.20 0.078
## [576,] 12.3 0.270 0.49 3.10 0.079
## [577,] 9.9 0.500 0.24 2.30 0.103
## [578,] 8.8 0.440 0.49 2.80 0.083
## [579,] 8.8 0.470 0.49 2.90 0.085
## [580,] 10.6 0.310 0.49 2.20 0.063
## [581,] 12.3 0.500 0.49 2.20 0.089
## [582,] 12.3 0.500 0.49 2.20 0.089
## [583,] 11.7 0.490 0.49 2.20 0.083
## [584,] 12.0 0.280 0.49 1.90 0.074
## [585,] 11.8 0.330 0.49 3.40 0.093
## [586,] 7.6 0.510 0.24 2.40 0.091
## [587,] 11.1 0.310 0.49 2.70 0.094
## [588,] 7.3 0.730 0.24 1.90 0.108
## [589,] 5.0 0.420 0.24 2.00 0.060
## [590,] 10.2 0.290 0.49 2.60 0.059
## [591,] 9.0 0.450 0.49 2.60 0.084
## [592,] 6.6 0.390 0.49 1.70 0.070
## [593,] 9.0 0.450 0.49 2.60 0.084
## [594,] 9.9 0.490 0.58 3.50 0.094
## [595,] 7.9 0.720 0.17 2.60 0.096
## [596,] 8.9 0.595 0.41 7.90 0.086
## [597,] 12.4 0.400 0.51 2.00 0.059
## [598,] 11.9 0.580 0.58 1.90 0.071
## [599,] 8.5 0.585 0.18 2.10 0.078
## [600,] 12.7 0.590 0.45 2.30 0.082
## [601,] 8.2 0.915 0.27 2.10 0.088
## [602,] 13.2 0.460 0.52 2.20 0.071
## [603,] 7.7 0.835 0.00 2.60 0.081
## [604,] 13.2 0.460 0.52 2.20 0.071
## [605,] 8.3 0.580 0.13 2.90 0.096
## [606,] 8.3 0.600 0.13 2.60 0.085
## [607,] 9.4 0.410 0.48 4.60 0.072
## [608,] 8.8 0.480 0.41 3.30 0.092
## [609,] 10.1 0.650 0.37 5.10 0.110
## [610,] 6.3 0.360 0.19 3.20 0.075
## [611,] 8.8 0.240 0.54 2.50 0.083
## [612,] 13.2 0.380 0.55 2.70 0.081
## [613,] 7.5 0.640 0.00 2.40 0.077
## [614,] 8.2 0.390 0.38 1.50 0.058
## [615,] 9.2 0.755 0.18 2.20 0.148
## [616,] 9.6 0.600 0.50 2.30 0.079
## [617,] 9.6 0.600 0.50 2.30 0.079
## [618,] 11.5 0.310 0.51 2.20 0.079
## [619,] 11.4 0.460 0.50 2.70 0.122
## [620,] 11.3 0.370 0.41 2.30 0.088
## [621,] 8.3 0.540 0.24 3.40 0.076
## [622,] 8.2 0.560 0.23 3.40 0.078
## [623,] 10.0 0.580 0.22 1.90 0.080
## [624,] 7.9 0.510 0.25 2.90 0.077
## [625,] 6.8 0.690 0.00 5.60 0.124
## [626,] 6.8 0.690 0.00 5.60 0.124
## [627,] 8.8 0.600 0.29 2.20 0.098
## [628,] 8.8 0.600 0.29 2.20 0.098
## [629,] 8.7 0.540 0.26 2.50 0.097
## [630,] 7.6 0.685 0.23 2.30 0.111
## [631,] 8.7 0.540 0.26 2.50 0.097
## [632,] 10.4 0.280 0.54 2.70 0.105
## [633,] 7.6 0.410 0.14 3.00 0.087
## [634,] 10.1 0.935 0.22 3.40 0.105
## [635,] 7.9 0.350 0.21 1.90 0.073
## [636,] 8.7 0.840 0.00 1.40 0.065
## [637,] 9.6 0.880 0.28 2.40 0.086
## [638,] 9.5 0.885 0.27 2.30 0.084
## [639,] 7.7 0.915 0.12 2.20 0.143
## [640,] 8.9 0.290 0.35 1.90 0.067
## [641,] 9.9 0.540 0.45 2.30 0.071
## [642,] 9.5 0.590 0.44 2.30 0.071
## [643,] 9.9 0.540 0.45 2.30 0.071
## [644,] 9.5 0.590 0.44 2.30 0.071
## [645,] 9.9 0.540 0.45 2.30 0.071
## [646,] 7.8 0.640 0.10 6.00 0.115
## [647,] 7.3 0.670 0.05 3.60 0.107
## [648,] 8.3 0.845 0.01 2.20 0.070
## [649,] 8.7 0.480 0.30 2.80 0.066
## [650,] 6.7 0.420 0.27 8.60 0.068
## [651,] 10.7 0.430 0.39 2.20 0.106
## [652,] 9.8 0.880 0.25 2.50 0.104
## [653,] 15.9 0.360 0.65 7.50 0.096
## [654,] 9.4 0.330 0.59 2.80 0.079
## [655,] 8.6 0.470 0.47 2.40 0.074
## [656,] 9.7 0.550 0.17 2.90 0.087
## [657,] 10.7 0.430 0.39 2.20 0.106
## [658,] 12.0 0.500 0.59 1.40 0.073
## [659,] 7.2 0.520 0.07 1.40 0.074
## [660,] 7.1 0.840 0.02 4.40 0.096
## [661,] 7.2 0.520 0.07 1.40 0.074
## [662,] 7.5 0.420 0.31 1.60 0.080
## [663,] 7.2 0.570 0.06 1.60 0.076
## [664,] 10.1 0.280 0.46 1.80 0.050
## [665,] 12.1 0.400 0.52 2.00 0.092
## [666,] 9.4 0.590 0.14 2.00 0.084
## [667,] 8.3 0.490 0.36 1.80 0.222
## [668,] 11.3 0.340 0.45 2.00 0.082
## [669,] 10.0 0.730 0.43 2.30 0.059
## [670,] 11.3 0.340 0.45 2.00 0.082
## [671,] 6.9 0.400 0.24 2.50 0.083
## [672,] 8.2 0.730 0.21 1.70 0.074
## [673,] 9.8 1.240 0.34 2.00 0.079
## [674,] 8.2 0.730 0.21 1.70 0.074
## [675,] 10.8 0.400 0.41 2.20 0.084
## [676,] 9.3 0.410 0.39 2.20 0.064
## [677,] 10.8 0.400 0.41 2.20 0.084
## [678,] 8.6 0.800 0.11 2.30 0.084
## [679,] 8.3 0.780 0.10 2.60 0.081
## [680,] 10.8 0.260 0.45 3.30 0.060
## [681,] 13.3 0.430 0.58 1.90 0.070
## [682,] 8.0 0.450 0.23 2.20 0.094
## [683,] 8.5 0.460 0.31 2.25 0.078
## [684,] 8.1 0.780 0.23 2.60 0.059
## [685,] 9.8 0.980 0.32 2.30 0.078
## [686,] 8.1 0.780 0.23 2.60 0.059
## [687,] 7.1 0.650 0.18 1.80 0.070
## [688,] 9.1 0.640 0.23 3.10 0.095
## [689,] 7.7 0.660 0.04 1.60 0.039
## [690,] 8.1 0.380 0.48 1.80 0.157
## [691,] 7.4 1.185 0.00 4.25 0.097
## [692,] 9.2 0.920 0.24 2.60 0.087
## [693,] 8.6 0.490 0.51 2.00 0.422
## [694,] 9.0 0.480 0.32 2.80 0.084
## [695,] 9.0 0.470 0.31 2.70 0.084
## [696,] 5.1 0.470 0.02 1.30 0.034
## [697,] 7.0 0.650 0.02 2.10 0.066
## [698,] 7.0 0.650 0.02 2.10 0.066
## [699,] 9.4 0.615 0.28 3.20 0.087
## [700,] 11.8 0.380 0.55 2.10 0.071
## [701,] 10.6 1.020 0.43 2.90 0.076
## [702,] 7.0 0.650 0.02 2.10 0.066
## [703,] 7.0 0.640 0.02 2.10 0.067
## [704,] 7.5 0.380 0.48 2.60 0.073
## [705,] 9.1 0.765 0.04 1.60 0.078
## [706,] 8.4 1.035 0.15 6.00 0.073
## [707,] 7.0 0.780 0.08 2.00 0.093
## [708,] 7.4 0.490 0.19 3.00 0.077
## [709,] 7.8 0.545 0.12 2.50 0.068
## [710,] 9.7 0.310 0.47 1.60 0.062
## [711,] 10.6 1.025 0.43 2.80 0.080
## [712,] 8.9 0.565 0.34 3.00 0.093
## [713,] 8.7 0.690 0.00 3.20 0.084
## [714,] 8.0 0.430 0.36 2.30 0.075
## [715,] 9.9 0.740 0.28 2.60 0.078
## [716,] 7.2 0.490 0.18 2.70 0.069
## [717,] 8.0 0.430 0.36 2.30 0.075
## [718,] 7.6 0.460 0.11 2.60 0.079
## [719,] 8.4 0.560 0.04 2.00 0.082
## [720,] 7.1 0.660 0.00 3.90 0.086
## [721,] 8.4 0.560 0.04 2.00 0.082
## [722,] 8.9 0.480 0.24 2.85 0.094
## [723,] 7.6 0.420 0.08 2.70 0.084
## [724,] 7.1 0.310 0.30 2.20 0.053
## [725,] 7.5 1.115 0.10 3.10 0.086
## [726,] 9.0 0.660 0.17 3.00 0.077
## [727,] 8.1 0.720 0.09 2.80 0.084
## [728,] 6.4 0.570 0.02 1.80 0.067
## [729,] 6.4 0.570 0.02 1.80 0.067
## [730,] 6.4 0.865 0.03 3.20 0.071
## [731,] 9.5 0.550 0.66 2.30 0.387
## [732,] 8.9 0.875 0.13 3.45 0.088
## [733,] 7.3 0.835 0.03 2.10 0.092
## [734,] 7.0 0.450 0.34 2.70 0.082
## [735,] 7.7 0.560 0.20 2.00 0.075
## [736,] 7.7 0.965 0.10 2.10 0.112
## [737,] 7.7 0.965 0.10 2.10 0.112
## [738,] 8.2 0.590 0.00 2.50 0.093
## [739,] 9.0 0.460 0.23 2.80 0.092
## [740,] 9.0 0.690 0.00 2.40 0.088
## [741,] 8.3 0.760 0.29 4.20 0.075
## [742,] 9.2 0.530 0.24 2.60 0.078
## [743,] 6.5 0.615 0.00 1.90 0.065
## [744,] 11.6 0.410 0.58 2.80 0.096
## [745,] 11.1 0.390 0.54 2.70 0.095
## [746,] 7.3 0.510 0.18 2.10 0.070
## [747,] 8.2 0.340 0.38 2.50 0.080
## [748,] 8.6 0.330 0.40 2.60 0.083
## [749,] 7.2 0.500 0.18 2.10 0.071
## [750,] 7.3 0.510 0.18 2.10 0.070
## [751,] 8.3 0.650 0.10 2.90 0.089
## [752,] 8.3 0.650 0.10 2.90 0.089
## [753,] 7.6 0.540 0.13 2.50 0.097
## [754,] 8.3 0.650 0.10 2.90 0.089
## [755,] 7.8 0.480 0.68 1.70 0.415
## [756,] 7.8 0.910 0.07 1.90 0.058
## [757,] 6.3 0.980 0.01 2.00 0.057
## [758,] 8.1 0.870 0.00 2.20 0.084
## [759,] 8.1 0.870 0.00 2.20 0.084
## [760,] 8.8 0.420 0.21 2.50 0.092
## [761,] 9.0 0.580 0.25 2.80 0.075
## [762,] 9.3 0.655 0.26 2.00 0.096
## [763,] 8.8 0.700 0.00 1.70 0.069
## [764,] 9.3 0.655 0.26 2.00 0.096
## [765,] 9.1 0.680 0.11 2.80 0.093
## [766,] 9.2 0.670 0.10 3.00 0.091
## [767,] 8.8 0.590 0.18 2.90 0.089
## [768,] 7.5 0.600 0.32 2.70 0.103
## [769,] 7.1 0.590 0.02 2.30 0.082
## [770,] 7.9 0.720 0.01 1.90 0.076
## [771,] 7.1 0.590 0.02 2.30 0.082
## [772,] 9.4 0.685 0.26 2.40 0.082
## [773,] 9.5 0.570 0.27 2.30 0.082
## [774,] 7.9 0.400 0.29 1.80 0.157
## [775,] 7.9 0.400 0.30 1.80 0.157
## [776,] 7.2 1.000 0.00 3.00 0.102
## [777,] 6.9 0.765 0.18 2.40 0.243
## [778,] 6.9 0.635 0.17 2.40 0.241
## [779,] 8.3 0.430 0.30 3.40 0.079
## [780,] 7.1 0.520 0.03 2.60 0.076
## [781,] 7.0 0.570 0.00 2.00 0.190
## [782,] 6.5 0.460 0.14 2.40 0.114
## [783,] 9.0 0.820 0.05 2.40 0.081
## [784,] 6.5 0.460 0.14 2.40 0.114
## [785,] 7.1 0.590 0.01 2.50 0.077
## [786,] 9.9 0.350 0.41 2.30 0.083
## [787,] 9.9 0.350 0.41 2.30 0.083
## [788,] 10.0 0.560 0.24 2.20 0.079
## [789,] 10.0 0.560 0.24 2.20 0.079
## [790,] 8.6 0.630 0.17 2.90 0.099
## [791,] 7.4 0.370 0.43 2.60 0.082
## [792,] 8.8 0.640 0.17 2.90 0.084
## [793,] 7.1 0.610 0.02 2.50 0.081
## [794,] 7.7 0.600 0.00 2.60 0.055
## [795,] 10.1 0.270 0.54 2.30 0.065
## [796,] 10.8 0.890 0.30 2.60 0.132
## [797,] 8.7 0.460 0.31 2.50 0.126
## [798,] 9.3 0.370 0.44 1.60 0.038
## [799,] 9.4 0.500 0.34 3.60 0.082
## [800,] 9.4 0.500 0.34 3.60 0.082
## [801,] 7.2 0.610 0.08 4.00 0.082
## [802,] 8.6 0.550 0.09 3.30 0.068
## [803,] 5.1 0.585 0.00 1.70 0.044
## [804,] 7.7 0.560 0.08 2.50 0.114
## [805,] 8.4 0.520 0.22 2.70 0.084
## [806,] 8.2 0.280 0.40 2.40 0.052
## [807,] 8.4 0.250 0.39 2.00 0.041
## [808,] 8.2 0.280 0.40 2.40 0.052
## [809,] 7.4 0.530 0.12 1.90 0.165
## [810,] 7.6 0.480 0.31 2.80 0.070
## [811,] 7.3 0.490 0.10 2.60 0.068
## [812,] 12.9 0.500 0.55 2.80 0.072
## [813,] 10.8 0.450 0.33 2.50 0.099
## [814,] 6.9 0.390 0.24 2.10 0.102
## [815,] 12.6 0.410 0.54 2.80 0.103
## [816,] 10.8 0.450 0.33 2.50 0.099
## [817,] 9.8 0.510 0.19 3.20 0.081
## [818,] 10.8 0.290 0.42 1.60 0.084
## [819,] 7.1 0.715 0.00 2.35 0.071
## [820,] 9.1 0.660 0.15 3.20 0.097
## [821,] 7.0 0.685 0.00 1.90 0.099
## [822,] 4.9 0.420 0.00 2.10 0.048
## [823,] 6.7 0.540 0.13 2.00 0.076
## [824,] 6.7 0.540 0.13 2.00 0.076
## [825,] 7.1 0.480 0.28 2.80 0.068
## [826,] 7.1 0.460 0.14 2.80 0.076
## [827,] 7.5 0.270 0.34 2.30 0.050
## [828,] 7.1 0.460 0.14 2.80 0.076
## [829,] 7.8 0.570 0.09 2.30 0.065
## [830,] 5.9 0.610 0.08 2.10 0.071
## [831,] 7.5 0.685 0.07 2.50 0.058
## [832,] 5.9 0.610 0.08 2.10 0.071
## [833,] 10.4 0.440 0.42 1.50 0.145
## [834,] 11.6 0.470 0.44 1.60 0.147
## [835,] 8.8 0.685 0.26 1.60 0.088
## [836,] 7.6 0.665 0.10 1.50 0.066
## [837,] 6.7 0.280 0.28 2.40 0.012
## [838,] 6.7 0.280 0.28 2.40 0.012
## [839,] 10.1 0.310 0.35 1.60 0.075
## [840,] 6.0 0.500 0.04 2.20 0.092
## [841,] 11.1 0.420 0.47 2.65 0.085
## [842,] 6.6 0.660 0.00 3.00 0.115
## [843,] 10.6 0.500 0.45 2.60 0.119
## [844,] 7.1 0.685 0.35 2.00 0.088
## [845,] 9.9 0.250 0.46 1.70 0.062
## [846,] 6.4 0.640 0.21 1.80 0.081
## [847,] 6.4 0.640 0.21 1.80 0.081
## [848,] 7.4 0.680 0.16 1.80 0.078
## [849,] 6.4 0.640 0.21 1.80 0.081
## [850,] 6.4 0.630 0.21 1.60 0.080
## [851,] 9.3 0.430 0.44 1.90 0.085
## [852,] 9.3 0.430 0.44 1.90 0.085
## [853,] 8.0 0.420 0.32 2.50 0.080
## [854,] 9.3 0.360 0.39 1.50 0.080
## [855,] 9.3 0.360 0.39 1.50 0.080
## [856,] 7.6 0.735 0.02 2.50 0.071
## [857,] 9.3 0.360 0.39 1.50 0.080
## [858,] 8.2 0.260 0.34 2.50 0.073
## [859,] 11.7 0.280 0.47 1.70 0.054
## [860,] 6.8 0.560 0.22 1.80 0.074
## [861,] 7.2 0.620 0.06 2.70 0.077
## [862,] 5.8 1.010 0.66 2.00 0.039
## [863,] 7.5 0.420 0.32 2.70 0.067
## [864,] 7.2 0.620 0.06 2.50 0.078
## [865,] 7.2 0.620 0.06 2.70 0.077
## [866,] 7.2 0.635 0.07 2.60 0.077
## [867,] 6.8 0.490 0.22 2.30 0.071
## [868,] 6.9 0.510 0.23 2.00 0.072
## [869,] 6.8 0.560 0.22 1.80 0.074
## [870,] 7.6 0.630 0.03 2.00 0.080
## [871,] 7.7 0.715 0.01 2.10 0.064
## [872,] 6.9 0.560 0.03 1.50 0.086
## [873,] 7.3 0.350 0.24 2.00 0.067
## [874,] 9.1 0.210 0.37 1.60 0.067
## [875,] 10.4 0.380 0.46 2.10 0.104
## [876,] 8.8 0.310 0.40 2.80 0.109
## [877,] 7.1 0.470 0.00 2.20 0.067
## [878,] 7.7 0.715 0.01 2.10 0.064
## [879,] 8.8 0.610 0.19 4.00 0.094
## [880,] 7.2 0.600 0.04 2.50 0.076
## [881,] 9.2 0.560 0.18 1.60 0.078
## [882,] 7.6 0.715 0.00 2.10 0.068
## [883,] 8.4 0.310 0.29 3.10 0.194
## [884,] 7.2 0.600 0.04 2.50 0.076
## [885,] 8.8 0.610 0.19 4.00 0.094
## [886,] 8.9 0.750 0.14 2.50 0.086
## [887,] 9.0 0.800 0.12 2.40 0.083
## [888,] 10.7 0.520 0.38 2.60 0.066
## [889,] 6.8 0.570 0.00 2.50 0.072
## [890,] 10.7 0.900 0.34 6.60 0.112
## [891,] 7.2 0.340 0.24 2.00 0.071
## [892,] 7.2 0.660 0.03 2.30 0.078
## [893,] 10.1 0.450 0.23 1.90 0.082
## [894,] 7.2 0.660 0.03 2.30 0.078
## [895,] 7.2 0.630 0.03 2.20 0.080
## [896,] 7.1 0.590 0.01 2.30 0.080
## [897,] 8.3 0.310 0.39 2.40 0.078
## [898,] 7.1 0.590 0.01 2.30 0.080
## [899,] 8.3 0.310 0.39 2.40 0.078
## [900,] 8.3 1.020 0.02 3.40 0.084
## [901,] 8.9 0.310 0.36 2.60 0.056
## [902,] 7.4 0.635 0.10 2.40 0.080
## [903,] 7.4 0.635 0.10 2.40 0.080
## [904,] 6.8 0.590 0.06 6.00 0.060
## [905,] 6.8 0.590 0.06 6.00 0.060
## [906,] 9.2 0.580 0.20 3.00 0.081
## [907,] 7.2 0.540 0.27 2.60 0.084
## [908,] 6.1 0.560 0.00 2.20 0.079
## [909,] 7.4 0.520 0.13 2.40 0.078
## [910,] 7.3 0.305 0.39 1.20 0.059
## [911,] 9.3 0.380 0.48 3.80 0.132
## [912,] 9.1 0.280 0.46 9.00 0.114
## [913,] 10.0 0.460 0.44 2.90 0.065
## [914,] 9.4 0.395 0.46 4.60 0.094
## [915,] 7.3 0.305 0.39 1.20 0.059
## [916,] 8.6 0.315 0.40 2.20 0.079
## [917,] 5.3 0.715 0.19 1.50 0.161
## [918,] 6.8 0.410 0.31 8.80 0.084
## [919,] 8.4 0.360 0.32 2.20 0.081
## [920,] 8.4 0.620 0.12 1.80 0.072
## [921,] 9.6 0.410 0.37 2.30 0.091
## [922,] 8.4 0.360 0.32 2.20 0.081
## [923,] 8.4 0.620 0.12 1.80 0.072
## [924,] 6.8 0.410 0.31 8.80 0.084
## [925,] 8.6 0.470 0.27 2.30 0.055
## [926,] 8.6 0.220 0.36 1.90 0.064
## [927,] 9.4 0.240 0.33 2.30 0.061
## [928,] 8.4 0.670 0.19 2.20 0.093
## [929,] 8.6 0.470 0.27 2.30 0.055
## [930,] 8.7 0.330 0.38 3.30 0.063
## [931,] 6.6 0.610 0.01 1.90 0.080
## [932,] 7.4 0.610 0.01 2.00 0.074
## [933,] 7.6 0.400 0.29 1.90 0.078
## [934,] 7.4 0.610 0.01 2.00 0.074
## [935,] 6.6 0.610 0.01 1.90 0.080
## [936,] 8.8 0.300 0.38 2.30 0.060
## [937,] 8.8 0.300 0.38 2.30 0.060
## [938,] 12.0 0.630 0.50 1.40 0.071
## [939,] 7.2 0.380 0.38 2.80 0.068
## [940,] 6.2 0.460 0.17 1.60 0.073
## [941,] 9.6 0.330 0.52 2.20 0.074
## [942,] 9.9 0.270 0.49 5.00 0.082
## [943,] 10.1 0.430 0.40 2.60 0.092
## [944,] 9.8 0.500 0.34 2.30 0.094
## [945,] 8.3 0.300 0.49 3.80 0.090
## [946,] 10.2 0.440 0.42 2.00 0.071
## [947,] 10.2 0.440 0.58 4.10 0.092
## [948,] 8.3 0.280 0.48 2.10 0.093
## [949,] 8.9 0.120 0.45 1.80 0.075
## [950,] 8.9 0.120 0.45 1.80 0.075
## [951,] 8.9 0.120 0.45 1.80 0.075
## [952,] 8.3 0.280 0.48 2.10 0.093
## [953,] 8.2 0.310 0.40 2.20 0.058
## [954,] 10.2 0.340 0.48 2.10 0.052
## [955,] 7.6 0.430 0.40 2.70 0.082
## [956,] 8.5 0.210 0.52 1.90 0.090
## [957,] 9.0 0.360 0.52 2.10 0.111
## [958,] 9.5 0.370 0.52 2.00 0.088
## [959,] 6.4 0.570 0.12 2.30 0.120
## [960,] 8.0 0.590 0.05 2.00 0.089
## [961,] 8.5 0.470 0.27 1.90 0.058
## [962,] 7.1 0.560 0.14 1.60 0.078
## [963,] 6.6 0.570 0.02 2.10 0.115
## [964,] 8.8 0.270 0.39 2.00 0.100
## [965,] 8.5 0.470 0.27 1.90 0.058
## [966,] 8.3 0.340 0.40 2.40 0.065
## [967,] 9.0 0.380 0.41 2.40 0.103
## [968,] 8.5 0.660 0.20 2.10 0.097
## [969,] 9.0 0.400 0.43 2.40 0.068
## [970,] 6.7 0.560 0.09 2.90 0.079
## [971,] 10.4 0.260 0.48 1.90 0.066
## [972,] 10.4 0.260 0.48 1.90 0.066
## [973,] 10.1 0.380 0.50 2.40 0.104
## [974,] 8.5 0.340 0.44 1.70 0.079
## [975,] 8.8 0.330 0.41 5.90 0.073
## [976,] 7.2 0.410 0.30 2.10 0.083
## [977,] 7.2 0.410 0.30 2.10 0.083
## [978,] 8.4 0.590 0.29 2.60 0.109
## [979,] 7.0 0.400 0.32 3.60 0.061
## [980,] 12.2 0.450 0.49 1.40 0.075
## [981,] 9.1 0.500 0.30 1.90 0.065
## [982,] 9.5 0.860 0.26 1.90 0.079
## [983,] 7.3 0.520 0.32 2.10 0.070
## [984,] 9.1 0.500 0.30 1.90 0.065
## [985,] 12.2 0.450 0.49 1.40 0.075
## [986,] 7.4 0.580 0.00 2.00 0.064
## [987,] 9.8 0.340 0.39 1.40 0.066
## [988,] 7.1 0.360 0.30 1.60 0.080
## [989,] 7.7 0.390 0.12 1.70 0.097
## [990,] 9.7 0.295 0.40 1.50 0.073
## [991,] 7.7 0.390 0.12 1.70 0.097
## [992,] 7.1 0.340 0.28 2.00 0.082
## [993,] 6.5 0.400 0.10 2.00 0.076
## [994,] 7.1 0.340 0.28 2.00 0.082
## [995,] 10.0 0.350 0.45 2.50 0.092
## [996,] 7.7 0.600 0.06 2.00 0.079
## [997,] 5.6 0.660 0.00 2.20 0.087
## [998,] 5.6 0.660 0.00 2.20 0.087
## [999,] 8.9 0.840 0.34 1.40 0.050
## [1000,] 6.4 0.690 0.00 1.65 0.055
## [1001,] 7.5 0.430 0.30 2.20 0.062
## [1002,] 9.9 0.350 0.38 1.50 0.058
## [1003,] 9.1 0.290 0.33 2.05 0.063
## [1004,] 6.8 0.360 0.32 1.80 0.067
## [1005,] 8.2 0.430 0.29 1.60 0.081
## [1006,] 6.8 0.360 0.32 1.80 0.067
## [1007,] 9.1 0.290 0.33 2.05 0.063
## [1008,] 9.1 0.300 0.34 2.00 0.064
## [1009,] 8.9 0.350 0.40 3.60 0.110
## [1010,] 9.6 0.500 0.36 2.80 0.116
## [1011,] 8.9 0.280 0.45 1.70 0.067
## [1012,] 8.9 0.320 0.31 2.00 0.088
## [1013,] 7.7 1.005 0.15 2.10 0.102
## [1014,] 7.5 0.710 0.00 1.60 0.092
## [1015,] 8.0 0.580 0.16 2.00 0.120
## [1016,] 10.5 0.390 0.46 2.20 0.075
## [1017,] 8.9 0.380 0.40 2.20 0.068
## [1018,] 8.0 0.180 0.37 0.90 0.049
## [1019,] 8.0 0.180 0.37 0.90 0.049
## [1020,] 7.0 0.500 0.14 1.80 0.078
## [1021,] 11.3 0.360 0.66 2.40 0.123
## [1022,] 11.3 0.360 0.66 2.40 0.123
## [1023,] 7.0 0.510 0.09 2.10 0.062
## [1024,] 8.2 0.320 0.42 2.30 0.098
## [1025,] 7.7 0.580 0.01 1.80 0.088
## [1026,] 8.6 0.830 0.00 2.80 0.095
## [1027,] 7.9 0.310 0.32 1.90 0.066
## [1028,] 6.4 0.795 0.00 2.20 0.065
## [1029,] 7.2 0.340 0.21 2.50 0.075
## [1030,] 7.7 0.580 0.01 1.80 0.088
## [1031,] 7.1 0.590 0.00 2.10 0.091
## [1032,] 7.3 0.550 0.01 1.80 0.093
## [1033,] 8.1 0.820 0.00 4.10 0.095
## [1034,] 7.5 0.570 0.08 2.60 0.089
## [1035,] 8.9 0.745 0.18 2.50 0.077
## [1036,] 10.1 0.370 0.34 2.40 0.085
## [1037,] 7.6 0.310 0.34 2.50 0.082
## [1038,] 7.3 0.910 0.10 1.80 0.074
## [1039,] 8.7 0.410 0.41 6.20 0.078
## [1040,] 8.9 0.500 0.21 2.20 0.088
## [1041,] 7.4 0.965 0.00 2.20 0.088
## [1042,] 6.9 0.490 0.19 1.70 0.079
## [1043,] 8.9 0.500 0.21 2.20 0.088
## [1044,] 9.5 0.390 0.41 8.90 0.069
## [1045,] 6.4 0.390 0.33 3.30 0.046
## [1046,] 6.9 0.440 0.00 1.40 0.070
## [1047,] 7.6 0.780 0.00 1.70 0.076
## [1048,] 7.1 0.430 0.17 1.80 0.082
## [1049,] 9.3 0.490 0.36 1.70 0.081
## [1050,] 9.3 0.500 0.36 1.80 0.084
## [1051,] 7.1 0.430 0.17 1.80 0.082
## [1052,] 8.5 0.460 0.59 1.40 0.414
## [1053,] 5.6 0.605 0.05 2.40 0.073
## [1054,] 8.3 0.330 0.42 2.30 0.070
## [1055,] 8.2 0.640 0.27 2.00 0.095
## [1056,] 8.2 0.640 0.27 2.00 0.095
## [1057,] 8.9 0.480 0.53 4.00 0.101
## [1058,] 7.6 0.420 0.25 3.90 0.104
## [1059,] 9.9 0.530 0.57 2.40 0.093
## [1060,] 8.9 0.480 0.53 4.00 0.101
## [1061,] 11.6 0.230 0.57 1.80 0.074
## [1062,] 9.1 0.400 0.50 1.80 0.071
## [1063,] 8.0 0.380 0.44 1.90 0.098
## [1064,] 10.2 0.290 0.65 2.40 0.075
## [1065,] 8.2 0.740 0.09 2.00 0.067
## [1066,] 7.7 0.610 0.18 2.40 0.083
## [1067,] 6.6 0.520 0.08 2.40 0.070
## [1068,] 11.1 0.310 0.53 2.20 0.060
## [1069,] 11.1 0.310 0.53 2.20 0.060
## [1070,] 8.0 0.620 0.35 2.80 0.086
## [1071,] 9.3 0.330 0.45 1.50 0.057
## [1072,] 7.5 0.770 0.20 8.10 0.098
## [1073,] 7.2 0.350 0.26 1.80 0.083
## [1074,] 8.0 0.620 0.33 2.70 0.088
## [1075,] 7.5 0.770 0.20 8.10 0.098
## [1076,] 9.1 0.250 0.34 2.00 0.071
## [1077,] 9.9 0.320 0.56 2.00 0.073
## [1078,] 8.6 0.370 0.65 6.40 0.080
## [1079,] 8.6 0.370 0.65 6.40 0.080
## [1080,] 7.9 0.300 0.68 8.30 0.050
## [1081,] 10.3 0.270 0.56 1.40 0.047
## [1082,] 7.9 0.300 0.68 8.30 0.050
## [1083,] 7.2 0.380 0.30 1.80 0.073
## [1084,] 8.7 0.420 0.45 2.40 0.072
## [1085,] 7.2 0.380 0.30 1.80 0.073
## [1086,] 6.8 0.480 0.08 1.80 0.074
## [1087,] 8.5 0.340 0.40 4.70 0.055
## [1088,] 7.9 0.190 0.42 1.60 0.057
## [1089,] 11.6 0.410 0.54 1.50 0.095
## [1090,] 11.6 0.410 0.54 1.50 0.095
## [1091,] 10.0 0.260 0.54 1.90 0.083
## [1092,] 7.9 0.340 0.42 2.00 0.086
## [1093,] 7.0 0.540 0.09 2.00 0.081
## [1094,] 9.2 0.310 0.36 2.20 0.079
## [1095,] 6.6 0.725 0.09 5.50 0.117
## [1096,] 9.4 0.400 0.47 2.50 0.087
## [1097,] 6.6 0.725 0.09 5.50 0.117
## [1098,] 8.6 0.520 0.38 1.50 0.096
## [1099,] 8.0 0.310 0.45 2.10 0.216
## [1100,] 8.6 0.520 0.38 1.50 0.096
## [1101,] 8.4 0.340 0.42 2.10 0.072
## [1102,] 7.4 0.490 0.27 2.10 0.071
## [1103,] 6.1 0.480 0.09 1.70 0.078
## [1104,] 7.4 0.490 0.27 2.10 0.071
## [1105,] 8.0 0.480 0.34 2.20 0.073
## [1106,] 6.3 0.570 0.28 2.10 0.048
## [1107,] 8.2 0.230 0.42 1.90 0.069
## [1108,] 9.1 0.300 0.41 2.00 0.068
## [1109,] 8.1 0.780 0.10 3.30 0.090
## [1110,] 10.8 0.470 0.43 2.10 0.171
## [1111,] 8.3 0.530 0.00 1.40 0.070
## [1112,] 5.4 0.420 0.27 2.00 0.092
## [1113,] 7.9 0.330 0.41 1.50 0.056
## [1114,] 8.9 0.240 0.39 1.60 0.074
## [1115,] 5.0 0.400 0.50 4.30 0.046
## [1116,] 7.0 0.690 0.07 2.50 0.091
## [1117,] 7.0 0.690 0.07 2.50 0.091
## [1118,] 7.0 0.690 0.07 2.50 0.091
## [1119,] 7.1 0.390 0.12 2.10 0.065
## [1120,] 5.6 0.660 0.00 2.50 0.066
## [1121,] 7.9 0.540 0.34 2.50 0.076
## [1122,] 6.6 0.500 0.00 1.80 0.062
## [1123,] 6.3 0.470 0.00 1.40 0.055
## [1124,] 10.7 0.400 0.37 1.90 0.081
## [1125,] 6.5 0.580 0.00 2.20 0.096
## [1126,] 8.8 0.240 0.35 1.70 0.055
## [1127,] 5.8 0.290 0.26 1.70 0.063
## [1128,] 6.3 0.760 0.00 2.90 0.072
## [1129,] 10.0 0.430 0.33 2.70 0.095
## [1130,] 10.5 0.430 0.35 3.30 0.092
## [1131,] 9.1 0.600 0.00 1.90 0.058
## [1132,] 5.9 0.190 0.21 1.70 0.045
## [1133,] 7.4 0.360 0.34 1.80 0.075
## [1134,] 7.2 0.480 0.07 5.50 0.089
## [1135,] 8.5 0.280 0.35 1.70 0.061
## [1136,] 8.0 0.250 0.43 1.70 0.067
## [1137,] 10.4 0.520 0.45 2.00 0.080
## [1138,] 10.4 0.520 0.45 2.00 0.080
## [1139,] 7.5 0.410 0.15 3.70 0.104
## [1140,] 8.2 0.510 0.24 2.00 0.079
## [1141,] 7.3 0.400 0.30 1.70 0.080
## [1142,] 8.2 0.380 0.32 2.50 0.080
## [1143,] 6.9 0.450 0.11 2.40 0.043
## [1144,] 7.0 0.220 0.30 1.80 0.065
## [1145,] 7.3 0.320 0.23 2.30 0.066
## [1146,] 8.2 0.200 0.43 2.50 0.076
## [1147,] 7.8 0.500 0.12 1.80 0.178
## [1148,] 10.0 0.410 0.45 6.20 0.071
## [1149,] 7.8 0.390 0.42 2.00 0.086
## [1150,] 10.0 0.350 0.47 2.00 0.061
## [1151,] 8.2 0.330 0.32 2.80 0.067
## [1152,] 6.1 0.580 0.23 2.50 0.044
## [1153,] 8.3 0.600 0.25 2.20 0.118
## [1154,] 9.6 0.420 0.35 2.10 0.083
## [1155,] 6.6 0.580 0.00 2.20 0.100
## [1156,] 8.3 0.600 0.25 2.20 0.118
## [1157,] 8.5 0.180 0.51 1.75 0.071
## [1158,] 5.1 0.510 0.18 2.10 0.042
## [1159,] 6.7 0.410 0.43 2.80 0.076
## [1160,] 10.2 0.410 0.43 2.20 0.110
## [1161,] 10.6 0.360 0.57 2.30 0.087
## [1162,] 8.8 0.450 0.43 1.40 0.076
## [1163,] 8.5 0.320 0.42 2.30 0.075
## [1164,] 9.0 0.785 0.24 1.70 0.078
## [1165,] 9.0 0.785 0.24 1.70 0.078
## [1166,] 8.5 0.440 0.50 1.90 0.369
## [1167,] 9.9 0.540 0.26 2.00 0.111
## [1168,] 8.2 0.330 0.39 2.50 0.074
## [1169,] 6.5 0.340 0.27 2.80 0.067
## [1170,] 7.6 0.500 0.29 2.30 0.086
## [1171,] 9.2 0.360 0.34 1.60 0.062
## [1172,] 7.1 0.590 0.00 2.20 0.078
## [1173,] 9.7 0.420 0.46 2.10 0.074
## [1174,] 7.6 0.360 0.31 1.70 0.079
## [1175,] 7.6 0.360 0.31 1.70 0.079
## [1176,] 6.5 0.610 0.00 2.20 0.095
## [1177,] 6.5 0.880 0.03 5.60 0.079
## [1178,] 7.1 0.660 0.00 2.40 0.052
## [1179,] 5.6 0.915 0.00 2.10 0.041
## [1180,] 8.2 0.350 0.33 2.40 0.076
## [1181,] 8.2 0.350 0.33 2.40 0.076
## [1182,] 9.8 0.390 0.43 1.65 0.068
## [1183,] 10.2 0.400 0.40 2.50 0.068
## [1184,] 6.8 0.660 0.07 1.60 0.070
## [1185,] 6.7 0.640 0.23 2.10 0.080
## [1186,] 7.0 0.430 0.30 2.00 0.085
## [1187,] 6.6 0.800 0.03 7.80 0.079
## [1188,] 7.0 0.430 0.30 2.00 0.085
## [1189,] 6.7 0.640 0.23 2.10 0.080
## [1190,] 8.8 0.955 0.05 1.80 0.075
## [1191,] 9.1 0.400 0.57 4.60 0.080
## [1192,] 6.5 0.885 0.00 2.30 0.166
## [1193,] 7.2 0.250 0.37 2.50 0.063
## [1194,] 6.4 0.885 0.00 2.30 0.166
## [1195,] 7.0 0.745 0.12 1.80 0.114
## [1196,] 6.2 0.430 0.22 1.80 0.078
## [1197,] 7.9 0.580 0.23 2.30 0.076
## [1198,] 7.7 0.570 0.21 1.50 0.069
## [1199,] 7.7 0.260 0.26 2.00 0.052
## [1200,] 7.9 0.580 0.23 2.30 0.076
## [1201,] 7.7 0.570 0.21 1.50 0.069
## [1202,] 7.9 0.340 0.36 1.90 0.065
## [1203,] 8.6 0.420 0.39 1.80 0.068
## [1204,] 9.9 0.740 0.19 5.80 0.111
## [1205,] 7.2 0.360 0.46 2.10 0.074
## [1206,] 7.2 0.360 0.46 2.10 0.074
## [1207,] 7.2 0.360 0.46 2.10 0.074
## [1208,] 9.9 0.720 0.55 1.70 0.136
## [1209,] 7.2 0.360 0.46 2.10 0.074
## [1210,] 6.2 0.390 0.43 2.00 0.071
## [1211,] 6.8 0.650 0.02 2.10 0.078
## [1212,] 6.6 0.440 0.15 2.10 0.076
## [1213,] 6.8 0.650 0.02 2.10 0.078
## [1214,] 9.6 0.380 0.42 1.90 0.071
## [1215,] 10.2 0.330 0.46 1.90 0.081
## [1216,] 8.8 0.270 0.46 2.10 0.095
## [1217,] 7.9 0.570 0.31 2.00 0.079
## [1218,] 8.2 0.340 0.37 1.90 0.057
## [1219,] 8.2 0.400 0.31 1.90 0.082
## [1220,] 9.0 0.390 0.40 1.30 0.044
## [1221,] 10.9 0.320 0.52 1.80 0.132
## [1222,] 10.9 0.320 0.52 1.80 0.132
## [1223,] 8.1 0.530 0.22 2.20 0.078
## [1224,] 10.5 0.360 0.47 2.20 0.074
## [1225,] 12.6 0.390 0.49 2.50 0.080
## [1226,] 9.2 0.460 0.23 2.60 0.091
## [1227,] 7.5 0.580 0.03 4.10 0.080
## [1228,] 9.0 0.580 0.25 2.00 0.104
## [1229,] 5.1 0.420 0.00 1.80 0.044
## [1230,] 7.6 0.430 0.29 2.10 0.075
## [1231,] 7.7 0.180 0.34 2.70 0.066
## [1232,] 7.8 0.815 0.01 2.60 0.074
## [1233,] 7.6 0.430 0.29 2.10 0.075
## [1234,] 10.2 0.230 0.37 2.20 0.057
## [1235,] 7.1 0.750 0.01 2.20 0.059
## [1236,] 6.0 0.330 0.32 12.90 0.054
## [1237,] 7.8 0.550 0.00 1.70 0.070
## [1238,] 7.1 0.750 0.01 2.20 0.059
## [1239,] 8.1 0.730 0.00 2.50 0.081
## [1240,] 6.5 0.670 0.00 4.30 0.057
## [1241,] 7.5 0.610 0.20 1.70 0.076
## [1242,] 9.8 0.370 0.39 2.50 0.079
## [1243,] 9.0 0.400 0.41 2.00 0.058
## [1244,] 8.3 0.560 0.22 2.40 0.082
## [1245,] 5.9 0.290 0.25 13.40 0.067
## [1246,] 7.4 0.550 0.19 1.80 0.082
## [1247,] 7.4 0.740 0.07 1.70 0.086
## [1248,] 7.4 0.550 0.19 1.80 0.082
## [1249,] 6.9 0.410 0.33 2.20 0.081
## [1250,] 7.1 0.600 0.01 2.30 0.079
## [1251,] 7.1 0.600 0.01 2.30 0.079
## [1252,] 7.5 0.580 0.14 2.20 0.077
## [1253,] 7.1 0.720 0.00 1.80 0.123
## [1254,] 7.9 0.660 0.00 1.40 0.096
## [1255,] 7.8 0.700 0.06 1.90 0.079
## [1256,] 6.1 0.640 0.02 2.40 0.069
## [1257,] 7.5 0.590 0.22 1.80 0.082
## [1258,] 7.0 0.580 0.28 4.80 0.085
## [1259,] 6.8 0.640 0.00 2.70 0.123
## [1260,] 6.8 0.640 0.00 2.70 0.123
## [1261,] 8.6 0.635 0.68 1.80 0.403
## [1262,] 6.3 1.020 0.00 2.00 0.083
## [1263,] 9.8 0.450 0.38 2.50 0.081
## [1264,] 8.2 0.780 0.00 2.20 0.089
## [1265,] 8.5 0.370 0.32 1.80 0.066
## [1266,] 7.2 0.570 0.05 2.30 0.081
## [1267,] 7.2 0.570 0.05 2.30 0.081
## [1268,] 10.4 0.430 0.50 2.30 0.068
## [1269,] 6.9 0.410 0.31 2.00 0.079
## [1270,] 5.5 0.490 0.03 1.80 0.044
## [1271,] 5.0 0.380 0.01 1.60 0.048
## [1272,] 7.3 0.440 0.20 1.60 0.049
## [1273,] 5.9 0.460 0.00 1.90 0.077
## [1274,] 7.5 0.580 0.20 2.00 0.073
## [1275,] 7.8 0.580 0.13 2.10 0.102
## [1276,] 8.0 0.715 0.22 2.30 0.075
## [1277,] 8.5 0.400 0.40 6.30 0.050
## [1278,] 7.0 0.690 0.00 1.90 0.114
## [1279,] 8.0 0.715 0.22 2.30 0.075
## [1280,] 9.8 0.300 0.39 1.70 0.062
## [1281,] 7.1 0.460 0.20 1.90 0.077
## [1282,] 7.1 0.460 0.20 1.90 0.077
## [1283,] 7.9 0.765 0.00 2.00 0.084
## [1284,] 8.7 0.630 0.28 2.70 0.096
## [1285,] 7.0 0.420 0.19 2.30 0.071
## [1286,] 11.3 0.370 0.50 1.80 0.090
## [1287,] 7.1 0.160 0.44 2.50 0.068
## [1288,] 8.0 0.600 0.08 2.60 0.056
## [1289,] 7.0 0.600 0.30 4.50 0.068
## [1290,] 7.0 0.600 0.30 4.50 0.068
## [1291,] 7.6 0.740 0.00 1.90 0.100
## [1292,] 8.2 0.635 0.10 2.10 0.073
## [1293,] 5.9 0.395 0.13 2.40 0.056
## [1294,] 7.5 0.755 0.00 1.90 0.084
## [1295,] 8.2 0.635 0.10 2.10 0.073
## [1296,] 6.6 0.630 0.00 4.30 0.093
## [1297,] 6.6 0.630 0.00 4.30 0.093
## [1298,] 7.2 0.530 0.14 2.10 0.064
## [1299,] 5.7 0.600 0.00 1.40 0.063
## [1300,] 7.6 1.580 0.00 2.10 0.137
## [1301,] 5.2 0.645 0.00 2.15 0.080
## [1302,] 6.7 0.860 0.07 2.00 0.100
## [1303,] 9.1 0.370 0.32 2.10 0.064
## [1304,] 8.0 0.280 0.44 1.80 0.081
## [1305,] 7.6 0.790 0.21 2.30 0.087
## [1306,] 7.5 0.610 0.26 1.90 0.073
## [1307,] 9.7 0.690 0.32 2.50 0.088
## [1308,] 6.8 0.680 0.09 3.90 0.068
## [1309,] 9.7 0.690 0.32 2.50 0.088
## [1310,] 7.0 0.620 0.10 1.40 0.071
## [1311,] 7.5 0.610 0.26 1.90 0.073
## [1312,] 6.5 0.510 0.15 3.00 0.064
## [1313,] 8.0 1.180 0.21 1.90 0.083
## [1314,] 7.0 0.360 0.21 2.30 0.086
## [1315,] 7.0 0.360 0.21 2.40 0.086
## [1316,] 7.5 0.630 0.27 2.00 0.083
## [1317,] 5.4 0.740 0.00 1.20 0.041
## [1318,] 9.9 0.440 0.46 2.20 0.091
## [1319,] 7.5 0.630 0.27 2.00 0.083
## [1320,] 9.1 0.760 0.68 1.70 0.414
## [1321,] 9.7 0.660 0.34 2.60 0.094
## [1322,] 5.0 0.740 0.00 1.20 0.041
## [1323,] 9.1 0.340 0.42 1.80 0.058
## [1324,] 9.1 0.360 0.39 1.80 0.060
## [1325,] 6.7 0.460 0.24 1.70 0.077
## [1326,] 6.7 0.460 0.24 1.70 0.077
## [1327,] 6.7 0.460 0.24 1.70 0.077
## [1328,] 6.7 0.460 0.24 1.70 0.077
## [1329,] 6.5 0.520 0.11 1.80 0.073
## [1330,] 7.4 0.600 0.26 2.10 0.083
## [1331,] 7.4 0.600 0.26 2.10 0.083
## [1332,] 7.8 0.870 0.26 3.80 0.107
## [1333,] 8.4 0.390 0.10 1.70 0.075
## [1334,] 9.1 0.775 0.22 2.20 0.079
## [1335,] 7.2 0.835 0.00 2.00 0.166
## [1336,] 6.6 0.580 0.02 2.40 0.069
## [1337,] 6.0 0.500 0.00 1.40 0.057
## [1338,] 6.0 0.500 0.00 1.40 0.057
## [1339,] 6.0 0.500 0.00 1.40 0.057
## [1340,] 7.5 0.510 0.02 1.70 0.084
## [1341,] 7.5 0.510 0.02 1.70 0.084
## [1342,] 7.5 0.510 0.02 1.70 0.084
## [1343,] 7.6 0.540 0.02 1.70 0.085
## [1344,] 7.5 0.510 0.02 1.70 0.084
## [1345,] 11.5 0.420 0.48 2.60 0.077
## [1346,] 8.2 0.440 0.24 2.30 0.063
## [1347,] 6.1 0.590 0.01 2.10 0.056
## [1348,] 7.2 0.655 0.03 1.80 0.078
## [1349,] 7.2 0.655 0.03 1.80 0.078
## [1350,] 6.9 0.570 0.00 2.80 0.081
## [1351,] 9.0 0.600 0.29 2.00 0.069
## [1352,] 7.2 0.620 0.01 2.30 0.065
## [1353,] 7.6 0.645 0.03 1.90 0.086
## [1354,] 7.6 0.645 0.03 1.90 0.086
## [1355,] 7.2 0.580 0.03 2.30 0.077
## [1356,] 6.1 0.320 0.25 1.80 0.086
## [1357,] 6.1 0.340 0.25 1.80 0.084
## [1358,] 7.3 0.430 0.24 2.50 0.078
## [1359,] 7.4 0.640 0.17 5.40 0.168
## [1360,] 11.6 0.475 0.40 1.40 0.091
## [1361,] 9.2 0.540 0.31 2.30 0.112
## [1362,] 8.3 0.850 0.14 2.50 0.093
## [1363,] 11.6 0.475 0.40 1.40 0.091
## [1364,] 8.0 0.830 0.27 2.00 0.080
## [1365,] 7.2 0.605 0.02 1.90 0.096
## [1366,] 7.8 0.500 0.09 2.20 0.115
## [1367,] 7.3 0.740 0.08 1.70 0.094
## [1368,] 6.9 0.540 0.30 2.20 0.088
## [1369,] 8.0 0.770 0.32 2.10 0.079
## [1370,] 6.6 0.610 0.00 1.60 0.069
## [1371,] 8.7 0.780 0.51 1.70 0.415
## [1372,] 7.5 0.580 0.56 3.10 0.153
## [1373,] 8.7 0.780 0.51 1.70 0.415
## [1374,] 7.7 0.750 0.27 3.80 0.110
## [1375,] 6.8 0.815 0.00 1.20 0.267
## [1376,] 7.2 0.560 0.26 2.00 0.083
## [1377,] 8.2 0.885 0.20 1.40 0.086
## [1378,] 5.2 0.490 0.26 2.30 0.090
## [1379,] 7.2 0.450 0.15 2.00 0.078
## [1380,] 7.5 0.570 0.02 2.60 0.077
## [1381,] 7.5 0.570 0.02 2.60 0.077
## [1382,] 6.8 0.830 0.09 1.80 0.074
## [1383,] 8.0 0.600 0.22 2.10 0.080
## [1384,] 8.0 0.600 0.22 2.10 0.080
## [1385,] 7.1 0.755 0.15 1.80 0.107
## [1386,] 8.0 0.810 0.25 3.40 0.076
## [1387,] 7.4 0.640 0.07 1.80 0.100
## [1388,] 7.4 0.640 0.07 1.80 0.100
## [1389,] 6.6 0.640 0.31 6.10 0.083
## [1390,] 6.7 0.480 0.02 2.20 0.080
## [1391,] 6.0 0.490 0.00 2.30 0.068
## [1392,] 8.0 0.640 0.22 2.40 0.094
## [1393,] 7.1 0.620 0.06 1.30 0.070
## [1394,] 8.0 0.520 0.25 2.00 0.078
## [1395,] 6.4 0.570 0.14 3.90 0.070
## [1396,] 8.6 0.685 0.10 1.60 0.092
## [1397,] 8.7 0.675 0.10 1.60 0.090
## [1398,] 7.3 0.590 0.26 2.00 0.080
## [1399,] 7.0 0.600 0.12 2.20 0.083
## [1400,] 7.2 0.670 0.00 2.20 0.068
## [1401,] 7.9 0.690 0.21 2.10 0.080
## [1402,] 7.9 0.690 0.21 2.10 0.080
## [1403,] 7.6 0.300 0.42 2.00 0.052
## [1404,] 7.2 0.330 0.33 1.70 0.061
## [1405,] 8.0 0.500 0.39 2.60 0.082
## [1406,] 7.7 0.280 0.30 2.00 0.062
## [1407,] 8.2 0.240 0.34 5.10 0.062
## [1408,] 6.0 0.510 0.00 2.10 0.064
## [1409,] 8.1 0.290 0.36 2.20 0.048
## [1410,] 6.0 0.510 0.00 2.10 0.064
## [1411,] 6.6 0.960 0.00 1.80 0.082
## [1412,] 6.4 0.470 0.40 2.40 0.071
## [1413,] 8.2 0.240 0.34 5.10 0.062
## [1414,] 9.9 0.570 0.25 2.00 0.104
## [1415,] 10.0 0.320 0.59 2.20 0.077
## [1416,] 6.2 0.580 0.00 1.60 0.065
## [1417,] 10.0 0.320 0.59 2.20 0.077
## [1418,] 7.3 0.340 0.33 2.50 0.064
## [1419,] 7.8 0.530 0.01 1.60 0.077
## [1420,] 7.7 0.640 0.21 2.20 0.077
## [1421,] 7.8 0.530 0.01 1.60 0.077
## [1422,] 7.5 0.400 0.18 1.60 0.079
## [1423,] 7.0 0.540 0.00 2.10 0.079
## [1424,] 6.4 0.530 0.09 3.90 0.123
## [1425,] 8.3 0.260 0.37 1.40 0.076
## [1426,] 8.3 0.260 0.37 1.40 0.076
## [1427,] 7.7 0.230 0.37 1.80 0.046
## [1428,] 7.6 0.410 0.33 2.50 0.078
## [1429,] 7.8 0.640 0.00 1.90 0.072
## [1430,] 7.9 0.180 0.40 2.20 0.049
## [1431,] 7.4 0.410 0.24 1.80 0.066
## [1432,] 7.6 0.430 0.31 2.10 0.069
## [1433,] 5.9 0.440 0.00 1.60 0.042
## [1434,] 6.1 0.400 0.16 1.80 0.069
## [1435,] 10.2 0.540 0.37 15.40 0.214
## [1436,] 10.2 0.540 0.37 15.40 0.214
## [1437,] 10.0 0.380 0.38 1.60 0.169
## [1438,] 6.8 0.915 0.29 4.80 0.070
## [1439,] 7.0 0.590 0.00 1.70 0.052
## [1440,] 7.3 0.670 0.02 2.20 0.072
## [1441,] 7.2 0.370 0.32 2.00 0.062
## [1442,] 7.4 0.785 0.19 5.20 0.094
## [1443,] 6.9 0.630 0.02 1.90 0.078
## [1444,] 6.9 0.580 0.20 1.75 0.058
## [1445,] 7.3 0.670 0.02 2.20 0.072
## [1446,] 7.4 0.785 0.19 5.20 0.094
## [1447,] 6.9 0.630 0.02 1.90 0.078
## [1448,] 6.8 0.670 0.00 1.90 0.080
## [1449,] 6.9 0.580 0.01 1.90 0.080
## [1450,] 7.2 0.380 0.31 2.00 0.056
## [1451,] 7.2 0.370 0.32 2.00 0.062
## [1452,] 7.8 0.320 0.44 2.70 0.104
## [1453,] 6.6 0.580 0.02 2.00 0.062
## [1454,] 7.6 0.490 0.33 1.90 0.074
## [1455,] 11.7 0.450 0.63 2.20 0.073
## [1456,] 6.5 0.900 0.00 1.60 0.052
## [1457,] 6.0 0.540 0.06 1.80 0.050
## [1458,] 7.6 0.490 0.33 1.90 0.074
## [1459,] 8.4 0.290 0.40 1.70 0.067
## [1460,] 7.9 0.200 0.35 1.70 0.054
## [1461,] 6.4 0.420 0.09 2.30 0.054
## [1462,] 6.2 0.785 0.00 2.10 0.060
## [1463,] 6.8 0.640 0.03 2.30 0.075
## [1464,] 6.9 0.630 0.01 2.40 0.076
## [1465,] 6.8 0.590 0.10 1.70 0.063
## [1466,] 6.8 0.590 0.10 1.70 0.063
## [1467,] 7.3 0.480 0.32 2.10 0.062
## [1468,] 6.7 1.040 0.08 2.30 0.067
## [1469,] 7.3 0.480 0.32 2.10 0.062
## [1470,] 7.3 0.980 0.05 2.10 0.061
## [1471,] 10.0 0.690 0.11 1.40 0.084
## [1472,] 6.7 0.700 0.08 3.75 0.067
## [1473,] 7.6 0.350 0.60 2.60 0.073
## [1474,] 6.1 0.600 0.08 1.80 0.071
## [1475,] 9.9 0.500 0.50 13.80 0.205
## [1476,] 5.3 0.470 0.11 2.20 0.048
## [1477,] 9.9 0.500 0.50 13.80 0.205
## [1478,] 5.3 0.470 0.11 2.20 0.048
## [1479,] 7.1 0.875 0.05 5.70 0.082
## [1480,] 8.2 0.280 0.60 3.00 0.104
## [1481,] 5.6 0.620 0.03 1.50 0.080
## [1482,] 8.2 0.280 0.60 3.00 0.104
## [1483,] 7.2 0.580 0.54 2.10 0.114
## [1484,] 8.1 0.330 0.44 1.50 0.042
## [1485,] 6.8 0.910 0.06 2.00 0.060
## [1486,] 7.0 0.655 0.16 2.10 0.074
## [1487,] 6.8 0.680 0.21 2.10 0.070
## [1488,] 6.0 0.640 0.05 1.90 0.066
## [1489,] 5.6 0.540 0.04 1.70 0.049
## [1490,] 6.2 0.570 0.10 2.10 0.048
## [1491,] 7.1 0.220 0.49 1.80 0.039
## [1492,] 5.6 0.540 0.04 1.70 0.049
## [1493,] 6.2 0.650 0.06 1.60 0.050
## [1494,] 7.7 0.540 0.26 1.90 0.089
## [1495,] 6.4 0.310 0.09 1.40 0.066
## [1496,] 7.0 0.430 0.02 1.90 0.080
## [1497,] 7.7 0.540 0.26 1.90 0.089
## [1498,] 6.9 0.740 0.03 2.30 0.054
## [1499,] 6.6 0.895 0.04 2.30 0.068
## [1500,] 6.9 0.740 0.03 2.30 0.054
## [1501,] 7.5 0.725 0.04 1.50 0.076
## [1502,] 7.8 0.820 0.29 4.30 0.083
## [1503,] 7.3 0.585 0.18 2.40 0.078
## [1504,] 6.2 0.440 0.39 2.50 0.077
## [1505,] 7.5 0.380 0.57 2.30 0.106
## [1506,] 6.7 0.760 0.02 1.80 0.078
## [1507,] 6.8 0.810 0.05 2.00 0.070
## [1508,] 7.5 0.380 0.57 2.30 0.106
## [1509,] 7.1 0.270 0.60 2.10 0.074
## [1510,] 7.9 0.180 0.40 1.80 0.062
## [1511,] 6.4 0.360 0.21 2.20 0.047
## [1512,] 7.1 0.690 0.04 2.10 0.068
## [1513,] 6.4 0.790 0.04 2.20 0.061
## [1514,] 6.4 0.560 0.15 1.80 0.078
## [1515,] 6.9 0.840 0.21 4.10 0.074
## [1516,] 6.9 0.840 0.21 4.10 0.074
## [1517,] 6.1 0.320 0.25 2.30 0.071
## [1518,] 6.5 0.530 0.06 2.00 0.063
## [1519,] 7.4 0.470 0.46 2.20 0.114
## [1520,] 6.6 0.700 0.08 2.60 0.106
## [1521,] 6.5 0.530 0.06 2.00 0.063
## [1522,] 6.9 0.480 0.20 1.90 0.082
## [1523,] 6.1 0.320 0.25 2.30 0.071
## [1524,] 6.8 0.480 0.25 2.00 0.076
## [1525,] 6.0 0.420 0.19 2.00 0.075
## [1526,] 6.7 0.480 0.08 2.10 0.064
## [1527,] 6.8 0.470 0.08 2.20 0.064
## [1528,] 7.1 0.530 0.07 1.70 0.071
## [1529,] 7.9 0.290 0.49 2.20 0.096
## [1530,] 7.1 0.690 0.08 2.10 0.063
## [1531,] 6.6 0.440 0.09 2.20 0.063
## [1532,] 6.1 0.705 0.10 2.80 0.081
## [1533,] 7.2 0.530 0.13 2.00 0.058
## [1534,] 8.0 0.390 0.30 1.90 0.074
## [1535,] 6.6 0.560 0.14 2.40 0.064
## [1536,] 7.0 0.550 0.13 2.20 0.075
## [1537,] 6.1 0.530 0.08 1.90 0.077
## [1538,] 5.4 0.580 0.08 1.90 0.059
## [1539,] 6.2 0.640 0.09 2.50 0.081
## [1540,] 7.2 0.390 0.32 1.80 0.065
## [1541,] 6.2 0.520 0.08 4.40 0.071
## [1542,] 7.4 0.250 0.29 2.20 0.054
## [1543,] 6.7 0.855 0.02 1.90 0.064
## [1544,] 11.1 0.440 0.42 2.20 0.064
## [1545,] 8.4 0.370 0.43 2.30 0.063
## [1546,] 6.5 0.630 0.33 1.80 0.059
## [1547,] 7.0 0.570 0.02 2.00 0.072
## [1548,] 6.3 0.600 0.10 1.60 0.048
## [1549,] 11.2 0.400 0.50 2.00 0.099
## [1550,] 7.4 0.360 0.30 1.80 0.074
## [1551,] 7.1 0.680 0.00 2.30 0.087
## [1552,] 7.1 0.670 0.00 2.30 0.083
## [1553,] 6.3 0.680 0.01 3.70 0.103
## [1554,] 7.3 0.735 0.00 2.20 0.080
## [1555,] 6.6 0.855 0.02 2.40 0.062
## [1556,] 7.0 0.560 0.17 1.70 0.065
## [1557,] 6.6 0.880 0.04 2.20 0.066
## [1558,] 6.6 0.855 0.02 2.40 0.062
## [1559,] 6.9 0.630 0.33 6.70 0.235
## [1560,] 7.8 0.600 0.26 2.00 0.080
## [1561,] 7.8 0.600 0.26 2.00 0.080
## [1562,] 7.8 0.600 0.26 2.00 0.080
## [1563,] 7.2 0.695 0.13 2.00 0.076
## [1564,] 7.2 0.695 0.13 2.00 0.076
## [1565,] 7.2 0.695 0.13 2.00 0.076
## [1566,] 6.7 0.670 0.02 1.90 0.061
## [1567,] 6.7 0.160 0.64 2.10 0.059
## [1568,] 7.2 0.695 0.13 2.00 0.076
## [1569,] 7.0 0.560 0.13 1.60 0.077
## [1570,] 6.2 0.510 0.14 1.90 0.056
## [1571,] 6.4 0.360 0.53 2.20 0.230
## [1572,] 6.4 0.380 0.14 2.20 0.038
## [1573,] 7.3 0.690 0.32 2.20 0.069
## [1574,] 6.0 0.580 0.20 2.40 0.075
## [1575,] 5.6 0.310 0.78 13.90 0.074
## [1576,] 7.5 0.520 0.40 2.20 0.060
## [1577,] 8.0 0.300 0.63 1.60 0.081
## [1578,] 6.2 0.700 0.15 5.10 0.076
## [1579,] 6.8 0.670 0.15 1.80 0.118
## [1580,] 6.2 0.560 0.09 1.70 0.053
## [1581,] 7.4 0.350 0.33 2.40 0.068
## [1582,] 6.2 0.560 0.09 1.70 0.053
## [1583,] 6.1 0.715 0.10 2.60 0.053
## [1584,] 6.2 0.460 0.29 2.10 0.074
## [1585,] 6.7 0.320 0.44 2.40 0.061
## [1586,] 7.2 0.390 0.44 2.60 0.066
## [1587,] 7.5 0.310 0.41 2.40 0.065
## [1588,] 5.8 0.610 0.11 1.80 0.066
## [1589,] 7.2 0.660 0.33 2.50 0.068
## [1590,] 6.6 0.725 0.20 7.80 0.073
## [1591,] 6.3 0.550 0.15 1.80 0.077
## [1592,] 5.4 0.740 0.09 1.70 0.089
## [1593,] 6.3 0.510 0.13 2.30 0.076
## [1594,] 6.8 0.620 0.08 1.90 0.068
## [1595,] 6.2 0.600 0.08 2.00 0.090
## [1596,] 5.9 0.550 0.10 2.20 0.062
## [1597,] 6.3 0.510 0.13 2.30 0.076
## [1598,] 5.9 0.645 0.12 2.00 0.075
## [1599,] 6.0 0.310 0.47 3.60 0.067
## free sulfur dioxide total sulfur dioxide density pH sulphates
## [1,] 11.0 34.0 0.99780 3.51 0.56
## [2,] 25.0 67.0 0.99680 3.20 0.68
## [3,] 15.0 54.0 0.99700 3.26 0.65
## [4,] 17.0 60.0 0.99800 3.16 0.58
## [5,] 11.0 34.0 0.99780 3.51 0.56
## [6,] 13.0 40.0 0.99780 3.51 0.56
## [7,] 15.0 59.0 0.99640 3.30 0.46
## [8,] 15.0 21.0 0.99460 3.39 0.47
## [9,] 9.0 18.0 0.99680 3.36 0.57
## [10,] 17.0 102.0 0.99780 3.35 0.80
## [11,] 15.0 65.0 0.99590 3.28 0.54
## [12,] 17.0 102.0 0.99780 3.35 0.80
## [13,] 16.0 59.0 0.99430 3.58 0.52
## [14,] 9.0 29.0 0.99740 3.26 1.56
## [15,] 52.0 145.0 0.99860 3.16 0.88
## [16,] 51.0 148.0 0.99860 3.17 0.93
## [17,] 35.0 103.0 0.99690 3.30 0.75
## [18,] 16.0 56.0 0.99680 3.11 1.28
## [19,] 6.0 29.0 0.99740 3.38 0.50
## [20,] 17.0 56.0 0.99690 3.04 1.08
## [21,] 29.0 60.0 0.99680 3.39 0.53
## [22,] 23.0 71.0 0.99820 3.52 0.65
## [23,] 10.0 37.0 0.99660 3.17 0.91
## [24,] 9.0 67.0 0.99680 3.17 0.53
## [25,] 21.0 40.0 0.99680 3.43 0.63
## [26,] 11.0 23.0 0.99550 3.34 0.56
## [27,] 4.0 11.0 0.99620 3.28 0.59
## [28,] 10.0 37.0 0.99660 3.17 0.91
## [29,] 14.0 35.0 0.99720 3.47 0.55
## [30,] 8.0 16.0 0.99640 3.38 0.59
## [31,] 17.0 82.0 0.99580 3.35 0.54
## [32,] 22.0 37.0 0.99660 3.46 0.57
## [33,] 15.0 113.0 0.99660 3.17 0.66
## [34,] 40.0 83.0 0.99930 3.45 0.52
## [35,] 13.0 50.0 0.99570 3.38 0.55
## [36,] 5.0 18.0 0.99860 3.40 0.55
## [37,] 3.0 15.0 0.99750 3.42 0.60
## [38,] 13.0 30.0 0.99680 3.23 0.73
## [39,] 7.0 19.0 0.99400 3.50 0.48
## [40,] 12.0 87.0 0.99780 3.33 0.83
## [41,] 12.0 87.0 0.99780 3.33 0.83
## [42,] 17.0 46.0 0.99760 3.26 0.51
## [43,] 8.0 14.0 0.99680 3.21 0.90
## [44,] 9.0 23.0 0.99680 3.30 1.20
## [45,] 5.0 11.0 0.99620 3.48 0.52
## [46,] 8.0 65.0 0.99340 3.90 0.56
## [47,] 22.0 114.0 0.99700 3.25 0.73
## [48,] 12.0 37.0 0.99690 3.25 0.58
## [49,] 5.0 12.0 0.99580 3.34 0.56
## [50,] 12.0 96.0 0.99540 3.32 0.58
## [51,] 4.0 23.0 0.99710 3.15 0.74
## [52,] 8.0 15.0 0.99560 3.40 0.63
## [53,] 6.0 14.0 0.99550 3.39 0.64
## [54,] 30.0 119.0 0.99700 3.20 0.56
## [55,] 33.0 73.0 0.99550 3.17 0.63
## [56,] 25.0 45.0 0.99780 3.34 0.53
## [57,] 4.0 10.0 0.99710 3.04 0.63
## [58,] 50.0 110.0 0.99830 3.26 0.77
## [59,] 17.0 54.0 0.99750 3.43 0.59
## [60,] 9.0 46.0 0.99620 3.41 0.54
## [61,] 19.0 52.0 0.99800 3.44 0.64
## [62,] 20.0 112.0 0.99680 3.21 0.71
## [63,] 12.0 35.0 0.99680 3.38 0.62
## [64,] 13.0 54.0 0.99660 3.39 0.57
## [65,] 4.0 11.0 0.99620 3.41 0.39
## [66,] 4.0 11.0 0.99620 3.41 0.39
## [67,] 11.0 39.0 0.99680 3.42 0.58
## [68,] 6.0 15.0 0.99620 3.44 0.58
## [69,] 27.0 65.0 0.99690 3.28 0.79
## [70,] 8.0 19.0 0.99620 3.34 0.95
## [71,] 15.0 27.0 0.99670 3.32 0.54
## [72,] 17.0 96.0 0.99620 3.32 0.48
## [73,] 18.0 94.0 0.99610 3.31 0.48
## [74,] 11.0 43.0 0.99760 3.31 0.53
## [75,] 28.0 83.0 0.99840 3.28 0.82
## [76,] 9.0 42.0 0.99860 3.54 0.66
## [77,] 9.0 42.0 0.99860 3.54 0.66
## [78,] 14.0 30.0 0.99660 3.52 0.55
## [79,] 12.0 80.0 0.99580 3.38 0.52
## [80,] 27.0 119.0 0.99720 3.16 1.12
## [81,] 3.0 15.0 0.99580 3.41 0.56
## [82,] 22.0 67.0 0.99740 3.13 1.28
## [83,] 21.0 73.0 0.99700 3.36 0.57
## [84,] 16.0 51.0 0.99690 3.16 1.14
## [85,] 18.0 61.0 0.99590 3.44 0.78
## [86,] 19.0 40.0 0.99610 3.41 0.59
## [87,] 20.0 136.0 0.99720 2.93 1.95
## [88,] 9.0 31.0 0.99660 3.39 0.64
## [89,] 34.0 125.0 0.99780 3.14 1.22
## [90,] 8.0 24.0 0.99780 3.48 0.53
## [91,] 42.0 140.0 0.99640 3.23 0.54
## [92,] 20.0 136.0 0.99720 2.93 1.95
## [93,] 19.0 133.0 0.99720 2.93 1.98
## [94,] 9.0 31.0 0.99660 3.39 0.64
## [95,] 41.0 85.0 0.99380 3.75 0.48
## [96,] 17.0 106.0 0.99320 3.85 0.60
## [97,] 8.0 23.0 0.99650 3.45 0.56
## [98,] 3.0 22.0 0.99630 3.25 0.63
## [99,] 5.0 10.0 0.99670 3.39 0.56
## [100,] 13.0 35.0 0.99720 3.30 0.59
## [101,] 11.0 50.0 0.99720 3.40 0.61
## [102,] 8.0 22.0 0.99590 3.31 0.56
## [103,] 13.0 35.0 0.99720 3.30 0.59
## [104,] 12.0 65.0 0.99670 3.29 0.51
## [105,] 5.0 36.0 0.99600 3.33 0.48
## [106,] 12.0 65.0 0.99670 3.29 0.51
## [107,] 18.0 69.0 0.99730 3.08 1.31
## [108,] 15.0 64.0 0.99690 3.46 0.79
## [109,] 18.0 80.0 0.99760 3.37 0.80
## [110,] 37.0 153.0 0.99690 3.21 0.69
## [111,] 12.0 47.0 0.99640 3.19 0.93
## [112,] 11.0 108.0 0.99640 3.15 0.66
## [113,] 14.0 111.0 0.99640 3.15 0.66
## [114,] 22.0 46.0 0.99880 3.32 0.67
## [115,] 12.0 47.0 0.99640 3.19 0.93
## [116,] 13.0 62.0 0.99660 3.07 0.63
## [117,] 11.0 40.0 0.99780 3.39 0.61
## [118,] 7.0 28.0 0.99700 3.37 0.50
## [119,] 14.0 56.0 0.99620 3.21 0.60
## [120,] 22.0 89.0 0.99590 3.34 0.54
## [121,] 10.0 89.0 0.99620 3.30 0.57
## [122,] 14.0 56.0 0.99620 3.21 0.60
## [123,] 3.0 13.0 0.99800 3.49 0.52
## [124,] 11.0 34.0 0.99760 3.44 0.53
## [125,] 21.0 102.0 0.99600 3.39 0.48
## [126,] 27.0 90.0 0.99840 3.16 0.70
## [127,] 3.0 12.0 0.99640 3.53 0.49
## [128,] 3.0 12.0 0.99640 3.54 0.48
## [129,] 3.0 16.0 0.99620 3.42 0.92
## [130,] 6.0 19.0 0.99550 3.42 0.57
## [131,] 30.0 134.0 0.99680 3.24 0.66
## [132,] 17.0 99.0 0.99370 3.63 0.63
## [133,] 17.0 99.0 0.99370 3.63 0.63
## [134,] 17.0 26.0 0.99520 3.40 0.58
## [135,] 13.0 29.0 0.99590 3.22 0.55
## [136,] 16.0 63.0 0.99650 3.19 0.82
## [137,] 10.0 52.0 0.99680 3.23 0.77
## [138,] 13.0 45.0 0.99720 3.48 0.64
## [139,] 15.0 105.0 0.99620 3.33 0.54
## [140,] 17.0 108.0 0.99620 3.32 0.54
## [141,] 16.0 63.0 0.99650 3.19 0.82
## [142,] 10.0 52.0 0.99680 3.23 0.77
## [143,] 27.0 63.0 0.99160 3.68 0.79
## [144,] 3.0 20.0 0.99540 3.34 0.58
## [145,] 27.0 63.0 0.99160 3.68 0.79
## [146,] 32.0 141.0 0.99680 3.17 0.62
## [147,] 21.0 94.0 0.99440 3.54 0.52
## [148,] 10.0 88.0 0.99680 3.11 0.80
## [149,] 12.0 30.0 0.99590 3.42 0.58
## [150,] 11.0 43.0 0.99750 3.53 0.61
## [151,] 5.0 11.0 0.99580 3.33 0.53
## [152,] 32.0 69.0 0.99960 2.74 2.00
## [153,] 25.0 99.0 0.99500 3.35 0.54
## [154,] 25.0 99.0 0.99500 3.35 0.54
## [155,] 29.0 129.0 0.99730 3.42 0.72
## [156,] 28.0 128.0 0.99730 3.42 0.71
## [157,] 29.0 129.0 0.99730 3.42 0.72
## [158,] 28.0 128.0 0.99730 3.42 0.71
## [159,] 12.0 22.0 0.99690 3.48 0.50
## [160,] 18.0 86.0 0.99680 3.59 0.57
## [161,] 7.0 20.0 0.99590 3.20 0.56
## [162,] 9.0 20.0 0.99650 3.17 1.08
## [163,] 17.0 31.0 0.99640 3.33 0.56
## [164,] 36.0 121.0 0.99820 3.37 0.49
## [165,] 35.0 121.0 0.99810 3.37 0.49
## [166,] 14.0 96.0 0.99610 3.19 0.62
## [167,] 18.0 101.0 0.99560 3.34 0.52
## [168,] 17.0 42.0 0.99560 3.37 0.48
## [169,] 11.0 44.0 0.99530 3.47 0.55
## [170,] 15.0 63.0 0.99640 3.00 1.59
## [171,] 4.0 8.0 0.99720 3.36 0.33
## [172,] 6.0 18.0 0.99720 3.29 0.61
## [173,] 6.0 18.0 0.99720 3.29 0.61
## [174,] 24.0 42.0 0.99610 3.42 0.57
## [175,] 7.0 35.0 0.99610 3.33 0.47
## [176,] 19.0 49.0 0.99580 3.35 0.78
## [177,] 7.0 35.0 0.99610 3.33 0.47
## [178,] 8.0 38.0 0.99720 3.58 0.61
## [179,] 7.0 20.0 0.99690 3.48 0.56
## [180,] 10.0 42.0 0.99690 3.19 0.59
## [181,] 10.0 42.0 0.99690 3.19 0.59
## [182,] 23.0 110.0 0.99720 3.12 1.02
## [183,] 16.0 42.0 0.99720 3.44 0.52
## [184,] 11.0 65.0 0.99710 3.54 0.58
## [185,] 8.0 62.0 0.99700 3.52 0.58
## [186,] 26.0 85.0 0.99710 3.26 0.53
## [187,] 14.0 67.0 0.99720 3.34 0.55
## [188,] 9.0 26.0 0.99760 3.39 0.49
## [189,] 15.0 143.0 0.99680 3.20 0.55
## [190,] 17.0 144.0 0.99680 3.20 0.55
## [191,] 21.0 127.0 0.99760 3.23 0.62
## [192,] 21.0 49.0 0.99740 3.57 0.62
## [193,] 16.0 126.0 0.99690 3.28 0.61
## [194,] 7.0 28.0 0.99640 3.28 0.55
## [195,] 7.0 28.0 0.99640 3.28 0.55
## [196,] 24.0 120.0 0.99680 3.25 0.54
## [197,] 15.0 55.0 0.99680 3.46 0.59
## [198,] 12.0 27.0 0.99810 3.11 0.97
## [199,] 13.0 93.0 0.99240 3.57 0.85
## [200,] 12.0 31.0 0.99480 3.51 0.43
## [201,] 9.0 24.0 0.99695 3.22 0.82
## [202,] 39.0 145.0 0.99750 3.04 1.03
## [203,] 16.0 49.0 0.99545 3.36 0.79
## [204,] 16.0 39.0 0.99610 3.34 0.55
## [205,] 14.0 37.0 0.99615 3.34 0.56
## [206,] 9.0 28.0 0.99940 3.20 0.77
## [207,] 9.0 28.0 0.99940 3.20 0.77
## [208,] 26.0 120.0 0.99625 3.29 0.53
## [209,] 18.0 95.0 0.99660 3.22 0.67
## [210,] 7.0 19.0 0.99800 3.31 0.88
## [211,] 5.0 19.0 0.99585 3.30 0.86
## [212,] 10.0 62.0 0.99685 3.35 0.56
## [213,] 5.0 15.0 0.99800 3.21 0.67
## [214,] 28.0 65.0 0.99590 3.30 0.43
## [215,] 10.0 41.0 0.99740 3.24 0.71
## [216,] 26.0 121.0 0.99740 3.34 0.76
## [217,] 13.0 49.0 0.99620 3.14 0.57
## [218,] 11.0 41.0 0.99670 3.36 0.55
## [219,] 10.0 44.0 0.99570 3.39 0.54
## [220,] 24.0 144.0 0.99655 3.30 0.60
## [221,] 24.0 58.0 0.99640 3.34 0.59
## [222,] 16.0 72.0 0.99570 3.15 0.57
## [223,] 5.0 10.0 0.99525 3.42 0.60
## [224,] 8.0 28.0 0.99815 3.28 0.60
## [225,] 15.0 55.0 0.99745 3.31 0.57
## [226,] 29.0 63.0 0.99615 3.37 0.58
## [227,] 27.0 81.0 0.99640 3.04 1.61
## [228,] 9.0 23.0 0.99840 3.39 0.63
## [229,] 29.0 63.0 0.99615 3.37 0.58
## [230,] 10.0 37.0 0.99685 3.46 0.50
## [231,] 19.0 106.0 0.99270 3.54 0.62
## [232,] 12.0 49.0 0.99625 3.37 0.52
## [233,] 18.0 58.0 0.99800 3.34 0.70
## [234,] 10.0 37.0 0.99685 3.46 0.50
## [235,] 7.0 37.0 0.99685 3.32 0.55
## [236,] 14.0 38.0 0.99675 3.37 0.58
## [237,] 14.0 38.0 0.99675 3.37 0.58
## [238,] 15.0 39.0 0.99675 3.37 0.58
## [239,] 14.0 38.0 0.99675 3.37 0.58
## [240,] 7.0 37.0 0.99685 3.32 0.55
## [241,] 5.0 62.0 0.99710 3.00 1.09
## [242,] 6.0 24.0 0.99925 3.14 0.71
## [243,] 28.0 109.0 0.99565 3.08 0.49
## [244,] 10.0 24.0 1.00005 3.07 0.84
## [245,] 10.0 24.0 1.00005 3.07 0.84
## [246,] 6.0 23.0 0.99830 3.61 0.96
## [247,] 16.0 51.0 0.99685 3.38 0.52
## [248,] 11.0 73.0 0.99630 3.20 0.45
## [249,] 9.0 39.0 0.99615 3.35 0.48
## [250,] 6.0 23.0 0.99830 3.61 0.96
## [251,] 16.0 37.0 0.99850 3.22 0.78
## [252,] 16.0 34.0 0.99720 3.47 0.70
## [253,] 5.0 21.0 0.99860 3.17 0.53
## [254,] 8.0 86.0 0.99590 3.23 0.59
## [255,] 16.0 34.0 0.99720 3.47 0.70
## [256,] 17.0 119.0 0.99675 3.26 0.57
## [257,] 5.0 13.0 0.99800 3.22 0.62
## [258,] 12.0 56.0 0.99680 3.49 0.67
## [259,] 8.0 45.0 0.99680 3.06 1.26
## [260,] 14.0 33.0 0.99965 3.36 0.80
## [261,] 18.0 45.0 0.99625 3.29 0.65
## [262,] 12.0 67.0 0.99565 3.35 0.60
## [263,] 10.0 35.0 0.99575 3.34 0.57
## [264,] 23.0 49.0 0.99630 3.28 0.67
## [265,] 5.0 27.0 0.99990 3.08 0.87
## [266,] 6.0 10.0 0.99680 3.20 0.72
## [267,] 26.0 61.0 1.00025 3.60 0.72
## [268,] 15.0 37.0 0.99730 3.35 0.86
## [269,] 7.0 27.0 0.99870 3.69 0.91
## [270,] 4.0 23.0 0.99960 3.28 0.97
## [271,] 27.0 61.0 0.99650 3.36 0.67
## [272,] 4.0 23.0 0.99960 3.28 0.97
## [273,] 17.0 65.0 0.99935 3.22 0.78
## [274,] 10.0 38.0 0.99735 3.31 0.64
## [275,] 27.0 94.0 0.99915 3.38 0.77
## [276,] 27.0 61.0 0.99650 3.36 0.67
## [277,] 7.0 27.0 0.99870 3.69 0.91
## [278,] 4.0 23.0 0.99960 3.28 0.97
## [279,] 5.0 13.0 0.99760 3.23 0.82
## [280,] 10.0 47.0 0.99910 3.38 0.77
## [281,] 6.0 19.0 0.99860 3.12 0.82
## [282,] 5.0 10.0 0.99720 3.25 1.08
## [283,] 12.0 53.0 0.99710 3.36 0.57
## [284,] 10.0 47.0 0.99910 3.38 0.77
## [285,] 32.0 71.0 1.00015 3.31 0.71
## [286,] 32.0 71.0 1.00015 3.31 0.71
## [287,] 25.0 49.0 0.99970 3.10 0.76
## [288,] 29.0 53.0 0.99670 3.37 0.70
## [289,] 20.0 49.0 0.99760 3.34 0.86
## [290,] 33.0 98.0 1.00100 3.20 0.95
## [291,] 20.0 49.0 0.99760 3.34 0.86
## [292,] 6.0 18.0 0.99790 3.30 0.71
## [293,] 18.0 48.0 0.99940 3.22 0.64
## [294,] 5.0 16.0 0.99640 3.41 0.60
## [295,] 17.0 53.0 1.00140 3.05 0.81
## [296,] 5.0 27.0 1.00010 3.05 0.64
## [297,] 26.0 70.0 0.99810 3.16 0.52
## [298,] 8.0 29.0 0.99855 3.67 0.73
## [299,] 5.0 31.0 0.99930 3.67 0.80
## [300,] 8.0 33.0 0.99845 3.68 0.71
## [301,] 20.0 44.0 0.99650 3.38 0.59
## [302,] 7.0 15.0 0.99730 3.22 0.64
## [303,] 12.0 28.0 0.99940 3.51 0.72
## [304,] 5.0 21.0 0.99600 3.39 0.54
## [305,] 12.0 90.0 0.99730 3.20 0.52
## [306,] 6.0 25.0 0.99980 3.12 0.59
## [307,] 7.0 54.0 0.99660 3.36 0.52
## [308,] 6.0 14.0 0.99940 3.19 0.62
## [309,] 5.0 12.0 0.99940 3.19 0.63
## [310,] 9.0 30.0 0.99680 3.41 0.53
## [311,] 6.0 25.0 0.99980 3.12 0.59
## [312,] 15.0 105.0 0.99600 3.27 0.54
## [313,] 19.0 98.0 0.99815 3.32 0.63
## [314,] 30.0 135.0 0.99760 3.30 0.53
## [315,] 26.0 72.0 0.99645 3.39 0.68
## [316,] 20.0 53.0 0.99620 3.42 0.65
## [317,] 37.0 92.0 0.99960 3.30 0.65
## [318,] 30.0 74.0 0.99865 3.30 0.64
## [319,] 21.0 59.0 0.99890 3.37 0.71
## [320,] 30.0 74.0 0.99865 3.30 0.64
## [321,] 21.0 59.0 0.99890 3.37 0.71
## [322,] 25.0 87.0 0.99975 3.24 0.62
## [323,] 6.0 18.0 0.99735 3.29 0.63
## [324,] 35.0 73.0 0.99900 3.28 0.70
## [325,] 13.0 50.0 1.00150 3.16 0.69
## [326,] 13.0 50.0 1.00150 3.16 0.69
## [327,] 6.0 14.0 0.99780 3.05 0.74
## [328,] 5.0 13.0 0.99800 3.28 0.83
## [329,] 6.0 21.0 1.00020 3.16 0.67
## [330,] 7.0 15.0 0.99810 3.18 0.62
## [331,] 10.0 41.0 0.99800 3.23 0.66
## [332,] 10.0 41.0 0.99800 3.23 0.66
## [333,] 21.0 114.0 0.99730 3.22 0.54
## [334,] 16.0 44.0 0.99580 3.13 0.52
## [335,] 17.0 38.0 0.99630 3.34 0.74
## [336,] 7.0 21.0 0.99920 3.17 0.84
## [337,] 6.0 16.0 0.99480 3.35 0.70
## [338,] 29.0 58.0 0.99740 3.31 0.64
## [339,] 28.0 99.0 1.00080 3.16 1.00
## [340,] 12.0 29.0 0.99970 3.11 1.36
## [341,] 10.0 21.0 1.00000 3.12 1.18
## [342,] 5.0 18.0 0.99720 3.21 0.87
## [343,] 6.0 14.0 0.99820 3.30 0.75
## [344,] 6.0 14.0 0.99820 3.30 0.75
## [345,] 6.0 32.0 1.00060 3.12 0.78
## [346,] 40.0 63.0 0.99790 3.60 0.81
## [347,] 17.0 34.0 0.99550 3.58 0.89
## [348,] 6.0 15.0 0.99860 3.02 0.93
## [349,] 15.0 46.0 0.99790 3.11 0.92
## [350,] 11.0 28.0 0.99940 3.36 0.86
## [351,] 17.0 34.0 1.00040 3.28 0.98
## [352,] 11.0 26.0 0.99940 3.35 0.83
## [353,] 8.0 19.0 0.99740 3.27 0.73
## [354,] 23.0 77.0 1.00180 3.18 0.77
## [355,] 40.5 165.0 0.99120 3.25 0.59
## [356,] 17.0 32.0 0.99550 3.55 0.61
## [357,] 29.0 55.0 1.00010 3.26 0.88
## [358,] 12.0 29.0 0.99700 3.24 0.75
## [359,] 10.0 23.0 1.00000 3.15 0.85
## [360,] 10.0 41.0 1.00100 3.17 0.68
## [361,] 14.0 81.0 0.99730 3.19 0.70
## [362,] 21.0 50.0 0.99820 3.37 0.91
## [363,] 6.0 37.0 0.99920 3.05 0.56
## [364,] 6.0 15.0 0.99880 2.99 0.87
## [365,] 7.0 42.0 1.00220 3.07 0.73
## [366,] 7.0 21.0 0.99790 3.26 0.93
## [367,] 7.0 42.0 1.00220 3.07 0.73
## [368,] 11.0 24.0 1.00000 3.16 0.69
## [369,] 15.0 75.0 0.99980 3.18 0.64
## [370,] 6.0 18.0 0.99620 3.20 1.13
## [371,] 35.0 63.0 0.99750 3.57 0.78
## [372,] 11.0 25.0 0.99670 3.32 0.87
## [373,] 26.0 46.0 0.99670 3.32 1.04
## [374,] 12.0 72.0 0.99590 3.05 0.63
## [375,] 6.0 47.0 1.00140 3.01 0.81
## [376,] 6.0 15.0 0.99840 3.01 0.83
## [377,] 19.0 47.0 1.00030 3.26 1.11
## [378,] 6.0 18.0 0.99620 3.20 1.13
## [379,] 6.0 24.0 0.99880 3.11 0.99
## [380,] 24.0 60.0 0.99790 3.31 0.70
## [381,] 11.0 27.0 0.99740 3.21 0.80
## [382,] 17.0 43.0 1.00140 3.06 0.80
## [383,] 11.0 27.0 0.99740 3.21 0.80
## [384,] 11.0 27.0 0.99740 3.21 0.80
## [385,] 23.0 54.0 0.99800 3.42 0.74
## [386,] 11.0 37.0 0.99790 3.43 0.76
## [387,] 23.0 48.0 0.99810 3.41 0.74
## [388,] 17.0 42.0 0.99720 3.31 0.54
## [389,] 23.0 53.0 0.99810 3.43 0.74
## [390,] 16.0 49.0 0.99820 3.19 0.70
## [391,] 12.0 88.0 0.99240 3.56 0.82
## [392,] 17.0 43.0 1.00140 3.06 0.80
## [393,] 6.0 26.0 0.99800 3.18 0.51
## [394,] 13.0 95.0 0.99700 3.16 0.54
## [395,] 6.0 25.0 0.99970 3.03 0.57
## [396,] 7.0 38.0 1.00040 3.22 0.60
## [397,] 68.0 124.0 0.99940 3.47 0.53
## [398,] 13.0 49.0 0.99880 3.18 0.65
## [399,] 13.0 49.0 0.99880 3.18 0.65
## [400,] 9.0 42.0 0.99630 3.10 0.55
## [401,] 68.0 124.0 0.99940 3.47 0.53
## [402,] 20.0 38.0 0.99490 3.29 0.47
## [403,] 19.0 64.0 1.00000 3.10 0.61
## [404,] 10.0 41.0 0.99940 3.15 0.63
## [405,] 15.0 27.0 0.99740 3.26 0.61
## [406,] 11.0 25.0 0.99660 3.36 0.76
## [407,] 9.0 28.0 0.99810 3.24 0.65
## [408,] 12.0 30.0 0.99960 3.18 0.63
## [409,] 6.0 16.0 0.99700 3.19 0.70
## [410,] 26.0 49.0 0.99810 3.05 0.57
## [411,] 26.0 86.0 0.99870 3.38 0.62
## [412,] 23.0 78.0 0.99870 3.38 0.62
## [413,] 15.0 77.0 0.99660 3.27 0.64
## [414,] 6.0 19.0 0.99860 3.27 0.82
## [415,] 24.0 122.0 0.99820 3.26 0.61
## [416,] 31.0 134.0 1.00140 3.32 1.07
## [417,] 6.0 20.0 0.99700 3.26 0.66
## [418,] 34.0 124.0 0.99560 3.44 0.48
## [419,] 7.0 20.0 0.99960 3.24 0.76
## [420,] 34.0 52.0 0.99760 3.62 0.68
## [421,] 35.0 67.0 0.99720 3.28 0.73
## [422,] 32.0 48.0 0.99520 3.52 0.56
## [423,] 15.0 60.0 0.99550 3.36 0.44
## [424,] 6.0 24.0 0.99780 3.15 0.90
## [425,] 15.0 60.0 0.99550 3.36 0.44
## [426,] 32.0 48.0 0.99520 3.52 0.56
## [427,] 19.0 48.0 0.99490 3.49 0.49
## [428,] 6.0 32.0 0.99880 3.26 0.56
## [429,] 9.0 30.0 0.99780 3.24 0.60
## [430,] 13.0 35.0 0.99970 3.10 0.60
## [431,] 6.0 24.0 0.99780 3.15 0.90
## [432,] 21.0 66.0 0.99740 3.25 0.56
## [433,] 12.0 24.0 0.99580 3.00 0.65
## [434,] 6.0 18.0 1.00040 3.16 0.49
## [435,] 22.0 54.0 0.99960 3.15 0.89
## [436,] 6.0 18.0 1.00040 3.16 0.49
## [437,] 38.0 62.0 0.99580 3.26 0.56
## [438,] 6.0 22.0 0.99860 3.17 0.66
## [439,] 22.0 54.0 0.99960 3.15 0.89
## [440,] 7.0 50.0 0.99510 3.08 0.60
## [441,] 6.0 29.0 0.99870 2.88 0.82
## [442,] 7.0 27.0 0.99880 3.06 0.68
## [443,] 6.0 43.0 1.00320 2.95 0.68
## [444,] 11.0 19.0 0.99630 3.23 0.63
## [445,] 5.0 27.0 0.99340 3.57 0.84
## [446,] 6.0 31.0 0.99860 3.23 0.56
## [447,] 31.0 72.0 0.99960 3.10 0.73
## [448,] 6.0 16.0 0.99680 3.22 0.72
## [449,] 7.0 27.0 0.99670 3.20 0.56
## [450,] 17.0 35.0 0.99940 3.10 0.61
## [451,] 17.0 35.0 0.99940 3.10 0.61
## [452,] 9.0 26.0 0.99790 3.06 1.06
## [453,] 18.0 35.0 0.99680 3.44 0.63
## [454,] 5.0 22.0 0.99980 3.26 0.74
## [455,] 21.0 67.0 0.99520 3.50 0.63
## [456,] 6.0 19.0 0.99880 3.22 0.69
## [457,] 5.0 22.0 0.99860 3.37 0.58
## [458,] 29.0 65.0 0.99880 3.28 0.58
## [459,] 5.0 22.0 0.99980 3.26 0.74
## [460,] 10.0 47.0 1.00080 3.25 0.57
## [461,] 14.0 23.0 0.99760 3.35 0.61
## [462,] 6.0 19.0 0.99820 3.26 0.61
## [463,] 10.0 25.0 0.99700 3.18 0.61
## [464,] 25.0 129.0 0.99720 3.08 0.53
## [465,] 5.0 15.0 0.99870 2.98 0.70
## [466,] 10.0 26.0 1.00060 3.48 0.91
## [467,] 21.0 51.0 0.99820 3.16 0.72
## [468,] 7.0 18.0 0.99470 3.32 0.79
## [469,] 6.0 21.0 1.00000 3.17 0.62
## [470,] 36.0 48.0 0.99520 3.20 0.58
## [471,] 15.0 47.0 0.99960 3.05 0.61
## [472,] 25.0 52.0 0.99700 3.20 0.71
## [473,] 25.0 68.0 0.99950 3.15 0.82
## [474,] 5.0 14.0 0.99710 3.26 0.79
## [475,] 10.0 24.0 0.99820 3.20 0.89
## [476,] 5.0 28.0 0.99880 3.14 0.60
## [477,] 6.0 16.0 0.99800 3.28 0.70
## [478,] 6.0 20.0 0.99770 3.18 1.06
## [479,] 5.0 28.0 0.99880 3.14 0.60
## [480,] 6.0 31.0 0.99840 3.19 0.70
## [481,] 6.0 23.0 1.00260 3.12 0.66
## [482,] 6.0 17.0 0.99640 3.15 0.92
## [483,] 6.0 18.0 0.99860 3.04 1.05
## [484,] 7.0 18.0 0.99860 3.04 1.06
## [485,] 6.0 24.0 0.99700 3.06 0.66
## [486,] 6.0 17.0 0.99760 3.17 0.47
## [487,] 6.0 17.0 0.99760 3.17 0.47
## [488,] 5.0 14.0 0.99820 3.17 0.42
## [489,] 35.0 67.0 1.00020 3.32 0.92
## [490,] 10.0 26.0 0.99840 3.34 0.75
## [491,] 24.0 56.0 0.99840 3.31 0.67
## [492,] 12.0 25.0 0.99520 3.34 0.79
## [493,] 13.0 27.0 0.99500 3.32 0.90
## [494,] 23.0 81.0 1.00020 3.48 0.74
## [495,] 28.0 91.0 0.99520 3.44 0.55
## [496,] 5.0 16.0 0.99720 3.15 0.65
## [497,] 14.0 38.0 0.99840 3.43 0.65
## [498,] 43.0 113.0 0.99660 3.32 0.79
## [499,] 5.0 16.0 0.99720 3.15 0.65
## [500,] 23.0 81.0 1.00020 3.48 0.74
## [501,] 14.0 38.0 0.99840 3.43 0.65
## [502,] 38.0 76.0 0.99900 3.17 0.85
## [503,] 38.0 76.0 0.99900 3.17 0.85
## [504,] 6.0 24.0 0.99760 3.18 1.04
## [505,] 6.0 22.0 0.99760 3.21 1.05
## [506,] 10.0 26.0 0.99680 3.16 0.78
## [507,] 6.0 21.0 0.99760 3.25 1.02
## [508,] 6.0 13.0 1.00000 3.17 0.71
## [509,] 26.0 62.0 0.99940 3.18 0.63
## [510,] 23.0 43.0 0.99860 3.04 0.68
## [511,] 19.0 43.0 0.99780 3.02 0.61
## [512,] 26.0 62.0 0.99940 3.18 0.63
## [513,] 15.0 49.0 0.99800 3.03 0.81
## [514,] 6.0 15.0 0.99730 3.09 0.66
## [515,] 6.0 15.0 0.99730 3.09 0.66
## [516,] 34.0 151.0 1.00100 3.31 1.14
## [517,] 5.0 14.0 1.00100 3.25 0.74
## [518,] 5.0 16.0 0.99940 3.16 0.63
## [519,] 11.0 32.0 0.99720 3.22 0.68
## [520,] 39.0 106.0 0.99660 3.36 0.78
## [521,] 15.0 33.0 0.99820 3.42 0.90
## [522,] 16.0 43.0 0.99800 3.48 0.64
## [523,] 47.0 133.0 0.99790 3.38 0.99
## [524,] 38.0 142.0 0.99780 3.22 0.55
## [525,] 23.0 116.0 0.99760 3.23 0.64
## [526,] 29.0 53.0 0.99980 3.24 0.67
## [527,] 39.0 106.0 0.99660 3.36 0.78
## [528,] 33.0 85.0 0.99620 3.39 0.77
## [529,] 32.0 55.0 0.99880 3.34 0.75
## [530,] 6.0 33.0 0.99740 3.09 0.57
## [531,] 1.0 28.0 0.99900 3.41 0.87
## [532,] 12.0 42.0 1.00040 3.16 0.61
## [533,] 12.0 42.0 1.00040 3.16 0.61
## [534,] 15.0 33.0 0.99560 3.22 0.66
## [535,] 13.0 32.0 1.00000 3.28 0.56
## [536,] 1.0 28.0 0.99900 3.41 0.87
## [537,] 6.0 33.0 0.99740 3.09 0.57
## [538,] 5.0 13.0 0.99720 3.37 0.77
## [539,] 5.0 35.0 1.00140 3.20 0.66
## [540,] 5.0 17.0 0.99960 3.22 0.62
## [541,] 20.0 47.0 0.99880 3.26 0.67
## [542,] 5.0 17.0 0.99880 3.21 0.73
## [543,] 5.0 20.0 0.99660 3.12 0.59
## [544,] 16.0 53.0 0.99930 3.06 0.72
## [545,] 6.0 15.0 1.00080 2.86 0.79
## [546,] 38.0 106.0 0.99820 3.08 0.59
## [547,] 10.0 28.0 0.99830 3.45 0.78
## [548,] 6.0 21.0 0.99870 3.26 0.86
## [549,] 27.0 69.0 0.99940 3.12 0.75
## [550,] 6.0 25.0 0.99750 3.27 0.61
## [551,] 9.0 25.0 0.99580 3.33 0.56
## [552,] 14.0 45.0 0.99800 3.19 0.73
## [553,] 14.0 44.0 0.99800 3.12 0.74
## [554,] 32.0 96.0 0.99340 3.74 0.62
## [555,] 10.0 23.0 1.00315 2.92 0.74
## [556,] 10.0 23.0 1.00315 2.92 0.74
## [557,] 10.0 17.0 1.00020 3.07 0.56
## [558,] 10.0 23.0 1.00315 2.92 0.74
## [559,] 10.0 17.0 1.00020 3.07 0.56
## [560,] 6.0 47.0 1.00210 3.30 0.68
## [561,] 5.0 19.0 0.99940 3.14 0.57
## [562,] 26.0 121.0 0.99780 3.23 0.58
## [563,] 41.0 110.0 0.99820 3.08 0.61
## [564,] 25.0 60.0 0.99710 3.31 0.61
## [565,] 6.0 47.0 1.00210 3.30 0.68
## [566,] 5.0 19.0 0.99940 3.14 0.57
## [567,] 5.0 15.0 0.99910 3.32 0.60
## [568,] 5.0 15.0 0.99910 3.32 0.60
## [569,] 5.0 20.0 0.99900 3.31 0.79
## [570,] 19.0 42.0 0.99460 3.57 0.57
## [571,] 10.0 37.0 1.00030 3.32 0.91
## [572,] 19.0 42.0 0.99460 3.57 0.57
## [573,] 10.0 28.0 0.99780 3.14 0.61
## [574,] 14.0 47.0 0.99910 3.30 0.56
## [575,] 20.0 78.0 0.99920 3.19 0.70
## [576,] 28.0 46.0 0.99930 3.20 0.80
## [577,] 6.0 14.0 0.99780 3.34 0.52
## [578,] 18.0 111.0 0.99820 3.30 0.60
## [579,] 17.0 110.0 0.99820 3.29 0.60
## [580,] 18.0 40.0 0.99760 3.14 0.51
## [581,] 5.0 14.0 1.00020 3.19 0.44
## [582,] 5.0 14.0 1.00020 3.19 0.44
## [583,] 5.0 15.0 1.00000 3.19 0.43
## [584,] 10.0 21.0 0.99760 2.98 0.66
## [585,] 54.0 80.0 1.00020 3.30 0.76
## [586,] 8.0 38.0 0.99800 3.47 0.66
## [587,] 16.0 47.0 0.99860 3.12 1.02
## [588,] 18.0 102.0 0.99670 3.26 0.59
## [589,] 19.0 50.0 0.99170 3.72 0.74
## [590,] 5.0 13.0 0.99760 3.05 0.74
## [591,] 21.0 75.0 0.99870 3.35 0.57
## [592,] 23.0 149.0 0.99220 3.12 0.50
## [593,] 21.0 75.0 0.99870 3.35 0.57
## [594,] 9.0 43.0 1.00040 3.29 0.58
## [595,] 20.0 38.0 0.99780 3.40 0.53
## [596,] 30.0 109.0 0.99980 3.27 0.57
## [597,] 6.0 24.0 0.99940 3.04 0.60
## [598,] 5.0 18.0 0.99800 3.09 0.63
## [599,] 5.0 30.0 0.99670 3.20 0.48
## [600,] 11.0 22.0 1.00000 3.00 0.70
## [601,] 7.0 23.0 0.99620 3.26 0.47
## [602,] 12.0 35.0 1.00060 3.10 0.56
## [603,] 6.0 14.0 0.99750 3.30 0.52
## [604,] 12.0 35.0 1.00060 3.10 0.56
## [605,] 14.0 63.0 0.99840 3.17 0.62
## [606,] 6.0 24.0 0.99840 3.31 0.59
## [607,] 10.0 20.0 0.99730 3.34 0.79
## [608,] 26.0 52.0 0.99820 3.31 0.53
## [609,] 11.0 65.0 1.00260 3.32 0.64
## [610,] 15.0 39.0 0.99560 3.56 0.52
## [611,] 25.0 57.0 0.99830 3.39 0.54
## [612,] 5.0 16.0 1.00060 2.98 0.54
## [613,] 18.0 29.0 0.99650 3.32 0.60
## [614,] 10.0 29.0 0.99620 3.26 0.74
## [615,] 10.0 103.0 0.99690 2.87 1.36
## [616,] 28.0 71.0 0.99970 3.50 0.57
## [617,] 28.0 71.0 0.99970 3.50 0.57
## [618,] 14.0 28.0 0.99820 3.03 0.93
## [619,] 4.0 17.0 1.00060 3.13 0.70
## [620,] 6.0 16.0 0.99880 3.09 0.80
## [621,] 16.0 112.0 0.99760 3.27 0.61
## [622,] 14.0 104.0 0.99760 3.28 0.62
## [623,] 9.0 32.0 0.99740 3.13 0.55
## [624,] 21.0 45.0 0.99740 3.49 0.96
## [625,] 21.0 58.0 0.99970 3.46 0.72
## [626,] 21.0 58.0 0.99970 3.46 0.72
## [627,] 5.0 15.0 0.99880 3.36 0.49
## [628,] 5.0 15.0 0.99880 3.36 0.49
## [629,] 7.0 31.0 0.99760 3.27 0.60
## [630,] 20.0 84.0 0.99640 3.21 0.61
## [631,] 7.0 31.0 0.99760 3.27 0.60
## [632,] 5.0 19.0 0.99880 3.25 0.63
## [633,] 21.0 43.0 0.99640 3.32 0.57
## [634,] 11.0 86.0 1.00100 3.43 0.64
## [635,] 46.0 102.0 0.99640 3.27 0.58
## [636,] 24.0 33.0 0.99540 3.27 0.55
## [637,] 30.0 147.0 0.99790 3.24 0.53
## [638,] 31.0 145.0 0.99780 3.24 0.53
## [639,] 7.0 23.0 0.99640 3.35 0.65
## [640,] 25.0 57.0 0.99700 3.18 1.36
## [641,] 16.0 40.0 0.99910 3.39 0.62
## [642,] 21.0 68.0 0.99920 3.46 0.63
## [643,] 16.0 40.0 0.99910 3.39 0.62
## [644,] 21.0 68.0 0.99920 3.46 0.63
## [645,] 16.0 40.0 0.99910 3.39 0.62
## [646,] 5.0 11.0 0.99840 3.37 0.69
## [647,] 6.0 20.0 0.99720 3.40 0.63
## [648,] 5.0 14.0 0.99670 3.32 0.58
## [649,] 10.0 28.0 0.99640 3.33 0.67
## [650,] 24.0 148.0 0.99480 3.16 0.57
## [651,] 8.0 32.0 0.99860 2.89 0.50
## [652,] 35.0 155.0 1.00100 3.41 0.67
## [653,] 22.0 71.0 0.99760 2.98 0.84
## [654,] 9.0 30.0 0.99760 3.12 0.54
## [655,] 7.0 29.0 0.99790 3.08 0.46
## [656,] 20.0 53.0 1.00040 3.14 0.61
## [657,] 8.0 32.0 0.99860 2.89 0.50
## [658,] 23.0 42.0 0.99800 2.92 0.68
## [659,] 5.0 20.0 0.99730 3.32 0.81
## [660,] 5.0 13.0 0.99700 3.41 0.57
## [661,] 5.0 20.0 0.99730 3.32 0.81
## [662,] 15.0 42.0 0.99780 3.31 0.64
## [663,] 9.0 27.0 0.99720 3.36 0.70
## [664,] 5.0 13.0 0.99740 3.04 0.79
## [665,] 15.0 54.0 1.00000 3.03 0.66
## [666,] 25.0 48.0 0.99810 3.14 0.56
## [667,] 6.0 16.0 0.99800 3.18 0.60
## [668,] 6.0 15.0 0.99880 2.94 0.66
## [669,] 15.0 31.0 0.99660 3.15 0.57
## [670,] 6.0 15.0 0.99880 2.94 0.66
## [671,] 30.0 45.0 0.99590 3.26 0.58
## [672,] 5.0 13.0 0.99680 3.20 0.52
## [673,] 32.0 151.0 0.99800 3.15 0.53
## [674,] 5.0 13.0 0.99680 3.20 0.52
## [675,] 7.0 17.0 0.99840 3.08 0.67
## [676,] 12.0 31.0 0.99840 3.26 0.65
## [677,] 7.0 17.0 0.99840 3.08 0.67
## [678,] 12.0 31.0 0.99790 3.40 0.48
## [679,] 45.0 87.0 0.99830 3.48 0.53
## [680,] 20.0 49.0 0.99720 3.13 0.54
## [681,] 15.0 40.0 1.00040 3.06 0.49
## [682,] 16.0 29.0 0.99620 3.21 0.49
## [683,] 32.0 58.0 0.99800 3.33 0.54
## [684,] 5.0 15.0 0.99700 3.37 0.56
## [685,] 35.0 152.0 0.99800 3.25 0.48
## [686,] 5.0 15.0 0.99700 3.37 0.56
## [687,] 13.0 40.0 0.99700 3.44 0.60
## [688,] 13.0 38.0 0.99980 3.28 0.59
## [689,] 4.0 9.0 0.99620 3.40 0.47
## [690,] 5.0 17.0 0.99760 3.30 1.05
## [691,] 5.0 14.0 0.99660 3.63 0.54
## [692,] 12.0 93.0 0.99980 3.48 0.54
## [693,] 16.0 62.0 0.99790 3.03 1.17
## [694,] 21.0 122.0 0.99840 3.32 0.62
## [695,] 24.0 125.0 0.99840 3.31 0.61
## [696,] 18.0 44.0 0.99210 3.90 0.62
## [697,] 8.0 25.0 0.99720 3.47 0.67
## [698,] 8.0 25.0 0.99720 3.47 0.67
## [699,] 18.0 72.0 1.00010 3.31 0.53
## [700,] 5.0 19.0 0.99860 3.11 0.62
## [701,] 26.0 88.0 0.99840 3.08 0.57
## [702,] 8.0 25.0 0.99720 3.47 0.67
## [703,] 9.0 23.0 0.99700 3.47 0.67
## [704,] 22.0 84.0 0.99720 3.32 0.70
## [705,] 4.0 14.0 0.99800 3.29 0.54
## [706,] 11.0 54.0 0.99900 3.37 0.49
## [707,] 10.0 19.0 0.99560 3.40 0.47
## [708,] 16.0 37.0 0.99660 3.37 0.51
## [709,] 11.0 35.0 0.99600 3.34 0.61
## [710,] 13.0 33.0 0.99830 3.27 0.66
## [711,] 21.0 84.0 0.99850 3.06 0.57
## [712,] 16.0 112.0 0.99980 3.38 0.61
## [713,] 13.0 33.0 0.99920 3.36 0.45
## [714,] 10.0 48.0 0.99760 3.34 0.46
## [715,] 21.0 77.0 0.99800 3.28 0.51
## [716,] 13.0 34.0 0.99670 3.29 0.48
## [717,] 10.0 48.0 0.99760 3.34 0.46
## [718,] 12.0 49.0 0.99680 3.21 0.57
## [719,] 10.0 22.0 0.99760 3.22 0.44
## [720,] 17.0 45.0 0.99760 3.46 0.54
## [721,] 10.0 22.0 0.99760 3.22 0.44
## [722,] 35.0 106.0 0.99820 3.10 0.53
## [723,] 15.0 48.0 0.99680 3.21 0.59
## [724,] 36.0 127.0 0.99650 2.94 1.62
## [725,] 5.0 12.0 0.99580 3.54 0.60
## [726,] 5.0 13.0 0.99760 3.29 0.55
## [727,] 18.0 49.0 0.99940 3.43 0.72
## [728,] 4.0 11.0 0.99700 3.46 0.68
## [729,] 4.0 11.0 0.99700 3.46 0.68
## [730,] 27.0 58.0 0.99500 3.61 0.49
## [731,] 12.0 37.0 0.99820 3.17 0.67
## [732,] 4.0 14.0 0.99940 3.44 0.52
## [733,] 10.0 19.0 0.99660 3.39 0.47
## [734,] 16.0 72.0 0.99800 3.55 0.60
## [735,] 9.0 39.0 0.99870 3.48 0.62
## [736,] 11.0 22.0 0.99630 3.26 0.50
## [737,] 11.0 22.0 0.99630 3.26 0.50
## [738,] 19.0 58.0 1.00020 3.50 0.65
## [739,] 28.0 104.0 0.99830 3.10 0.56
## [740,] 19.0 38.0 0.99900 3.35 0.60
## [741,] 12.0 16.0 0.99650 3.45 0.68
## [742,] 28.0 139.0 0.99788 3.21 0.57
## [743,] 9.0 18.0 0.99720 3.46 0.65
## [744,] 25.0 101.0 1.00024 3.13 0.53
## [745,] 21.0 101.0 1.00010 3.13 0.51
## [746,] 12.0 28.0 0.99768 3.52 0.73
## [747,] 12.0 57.0 0.99780 3.30 0.47
## [748,] 16.0 68.0 0.99782 3.30 0.48
## [749,] 12.0 31.0 0.99761 3.52 0.72
## [750,] 12.0 28.0 0.99768 3.52 0.73
## [751,] 17.0 40.0 0.99803 3.29 0.55
## [752,] 17.0 40.0 0.99803 3.29 0.55
## [753,] 24.0 66.0 0.99785 3.39 0.61
## [754,] 17.0 40.0 0.99803 3.29 0.55
## [755,] 14.0 32.0 0.99656 3.09 1.06
## [756,] 22.0 47.0 0.99525 3.51 0.43
## [757,] 15.0 33.0 0.99488 3.60 0.46
## [758,] 10.0 31.0 0.99656 3.25 0.50
## [759,] 10.0 31.0 0.99656 3.25 0.50
## [760,] 33.0 88.0 0.99823 3.19 0.52
## [761,] 9.0 104.0 0.99779 3.23 0.57
## [762,] 5.0 35.0 0.99738 3.25 0.42
## [763,] 8.0 19.0 0.99701 3.31 0.53
## [764,] 5.0 35.0 0.99738 3.25 0.42
## [765,] 11.0 44.0 0.99888 3.31 0.55
## [766,] 12.0 48.0 0.99888 3.31 0.54
## [767,] 12.0 74.0 0.99738 3.14 0.54
## [768,] 13.0 98.0 0.99938 3.45 0.62
## [769,] 24.0 94.0 0.99744 3.55 0.53
## [770,] 7.0 32.0 0.99668 3.39 0.54
## [771,] 24.0 94.0 0.99744 3.55 0.53
## [772,] 23.0 143.0 0.99780 3.28 0.55
## [773,] 23.0 144.0 0.99782 3.27 0.55
## [774,] 1.0 44.0 0.99730 3.30 0.92
## [775,] 2.0 45.0 0.99727 3.31 0.91
## [776,] 7.0 16.0 0.99586 3.43 0.46
## [777,] 5.5 48.0 0.99612 3.40 0.60
## [778,] 6.0 18.0 0.99610 3.40 0.59
## [779,] 7.0 34.0 0.99788 3.36 0.61
## [780,] 21.0 92.0 0.99745 3.50 0.60
## [781,] 12.0 45.0 0.99676 3.31 0.60
## [782,] 9.0 37.0 0.99732 3.66 0.65
## [783,] 26.0 96.0 0.99814 3.36 0.53
## [784,] 9.0 37.0 0.99732 3.66 0.65
## [785,] 20.0 85.0 0.99746 3.55 0.59
## [786,] 11.0 61.0 0.99820 3.21 0.50
## [787,] 11.0 61.0 0.99820 3.21 0.50
## [788,] 19.0 58.0 0.99910 3.18 0.56
## [789,] 19.0 58.0 0.99910 3.18 0.56
## [790,] 21.0 119.0 0.99800 3.09 0.52
## [791,] 18.0 82.0 0.99708 3.33 0.68
## [792,] 25.0 130.0 0.99818 3.23 0.54
## [793,] 17.0 87.0 0.99745 3.48 0.60
## [794,] 7.0 13.0 0.99639 3.38 0.56
## [795,] 7.0 26.0 0.99531 3.17 0.53
## [796,] 7.0 60.0 0.99786 2.99 1.18
## [797,] 24.0 64.0 0.99746 3.10 0.74
## [798,] 21.0 42.0 0.99526 3.24 0.81
## [799,] 5.0 14.0 0.99870 3.29 0.52
## [800,] 5.0 14.0 0.99870 3.29 0.52
## [801,] 26.0 108.0 0.99641 3.25 0.51
## [802,] 8.0 17.0 0.99735 3.23 0.44
## [803,] 14.0 86.0 0.99264 3.56 0.94
## [804,] 14.0 46.0 0.99710 3.24 0.66
## [805,] 4.0 18.0 0.99682 3.26 0.57
## [806,] 4.0 10.0 0.99356 3.33 0.70
## [807,] 4.0 10.0 0.99386 3.27 0.71
## [808,] 4.0 10.0 0.99356 3.33 0.70
## [809,] 4.0 12.0 0.99702 3.26 0.86
## [810,] 4.0 15.0 0.99693 3.22 0.55
## [811,] 4.0 14.0 0.99562 3.30 0.47
## [812,] 7.0 24.0 1.00012 3.09 0.68
## [813,] 20.0 38.0 0.99818 3.24 0.71
## [814,] 4.0 7.0 0.99462 3.44 0.58
## [815,] 19.0 41.0 0.99939 3.21 0.76
## [816,] 20.0 38.0 0.99818 3.24 0.71
## [817,] 8.0 30.0 0.99840 3.23 0.58
## [818,] 19.0 27.0 0.99545 3.28 0.73
## [819,] 21.0 47.0 0.99632 3.29 0.45
## [820,] 9.0 59.0 0.99976 3.28 0.54
## [821,] 9.0 22.0 0.99606 3.34 0.60
## [822,] 16.0 42.0 0.99154 3.71 0.74
## [823,] 15.0 36.0 0.99730 3.61 0.64
## [824,] 15.0 36.0 0.99730 3.61 0.64
## [825,] 6.0 16.0 0.99682 3.24 0.53
## [826,] 15.0 37.0 0.99624 3.36 0.49
## [827,] 4.0 8.0 0.99510 3.40 0.64
## [828,] 15.0 37.0 0.99624 3.36 0.49
## [829,] 34.0 45.0 0.99417 3.46 0.74
## [830,] 16.0 24.0 0.99376 3.56 0.77
## [831,] 5.0 9.0 0.99632 3.38 0.55
## [832,] 16.0 24.0 0.99376 3.56 0.77
## [833,] 34.0 48.0 0.99832 3.38 0.86
## [834,] 36.0 51.0 0.99836 3.38 0.86
## [835,] 16.0 23.0 0.99694 3.32 0.47
## [836,] 27.0 55.0 0.99655 3.39 0.51
## [837,] 36.0 100.0 0.99064 3.26 0.39
## [838,] 36.0 100.0 0.99064 3.26 0.39
## [839,] 9.0 28.0 0.99672 3.24 0.83
## [840,] 13.0 26.0 0.99647 3.46 0.47
## [841,] 9.0 34.0 0.99736 3.24 0.77
## [842,] 21.0 31.0 0.99629 3.45 0.63
## [843,] 34.0 68.0 0.99708 3.23 0.72
## [844,] 9.0 92.0 0.99630 3.28 0.62
## [845,] 26.0 42.0 0.99590 3.18 0.83
## [846,] 14.0 31.0 0.99689 3.59 0.66
## [847,] 14.0 31.0 0.99689 3.59 0.66
## [848,] 12.0 39.0 0.99770 3.50 0.70
## [849,] 14.0 31.0 0.99689 3.59 0.66
## [850,] 12.0 32.0 0.99689 3.58 0.66
## [851,] 9.0 22.0 0.99708 3.28 0.55
## [852,] 9.0 22.0 0.99708 3.28 0.55
## [853,] 26.0 122.0 0.99801 3.22 1.07
## [854,] 41.0 55.0 0.99652 3.47 0.73
## [855,] 41.0 55.0 0.99652 3.47 0.73
## [856,] 10.0 14.0 0.99538 3.51 0.71
## [857,] 41.0 55.0 0.99652 3.47 0.73
## [858,] 16.0 47.0 0.99594 3.40 0.78
## [859,] 17.0 32.0 0.99686 3.15 0.67
## [860,] 15.0 24.0 0.99438 3.40 0.82
## [861,] 15.0 85.0 0.99746 3.51 0.54
## [862,] 15.0 88.0 0.99357 3.66 0.60
## [863,] 7.0 25.0 0.99628 3.24 0.44
## [864,] 17.0 84.0 0.99746 3.51 0.53
## [865,] 15.0 85.0 0.99746 3.51 0.54
## [866,] 16.0 86.0 0.99748 3.51 0.54
## [867,] 13.0 24.0 0.99438 3.41 0.83
## [868,] 13.0 22.0 0.99438 3.40 0.84
## [869,] 15.0 24.0 0.99438 3.40 0.82
## [870,] 27.0 43.0 0.99578 3.44 0.64
## [871,] 31.0 43.0 0.99371 3.41 0.57
## [872,] 36.0 46.0 0.99522 3.53 0.57
## [873,] 28.0 48.0 0.99576 3.43 0.54
## [874,] 6.0 10.0 0.99552 3.23 0.58
## [875,] 6.0 10.0 0.99664 3.12 0.65
## [876,] 7.0 16.0 0.99614 3.31 0.79
## [877,] 7.0 14.0 0.99517 3.40 0.58
## [878,] 31.0 43.0 0.99371 3.41 0.57
## [879,] 30.0 69.0 0.99787 3.22 0.50
## [880,] 18.0 88.0 0.99745 3.53 0.55
## [881,] 10.0 21.0 0.99576 3.15 0.49
## [882,] 30.0 35.0 0.99533 3.48 0.65
## [883,] 14.0 26.0 0.99536 3.22 0.78
## [884,] 18.0 88.0 0.99745 3.53 0.55
## [885,] 30.0 69.0 0.99787 3.22 0.50
## [886,] 9.0 30.0 0.99824 3.34 0.64
## [887,] 8.0 28.0 0.99836 3.33 0.65
## [888,] 29.0 56.0 0.99577 3.15 0.79
## [889,] 32.0 64.0 0.99491 3.43 0.56
## [890,] 23.0 99.0 1.00289 3.22 0.68
## [891,] 30.0 52.0 0.99576 3.44 0.58
## [892,] 16.0 86.0 0.99743 3.53 0.57
## [893,] 10.0 18.0 0.99774 3.22 0.65
## [894,] 16.0 86.0 0.99743 3.53 0.57
## [895,] 17.0 88.0 0.99745 3.53 0.58
## [896,] 27.0 43.0 0.99550 3.42 0.58
## [897,] 17.0 43.0 0.99444 3.31 0.77
## [898,] 27.0 43.0 0.99550 3.42 0.58
## [899,] 17.0 43.0 0.99444 3.31 0.77
## [900,] 6.0 11.0 0.99892 3.48 0.49
## [901,] 10.0 39.0 0.99562 3.40 0.69
## [902,] 16.0 33.0 0.99736 3.58 0.69
## [903,] 16.0 33.0 0.99736 3.58 0.69
## [904,] 11.0 18.0 0.99620 3.41 0.59
## [905,] 11.0 18.0 0.99620 3.41 0.59
## [906,] 15.0 115.0 0.99800 3.23 0.59
## [907,] 12.0 78.0 0.99640 3.39 0.71
## [908,] 6.0 9.0 0.99480 3.59 0.54
## [909,] 34.0 61.0 0.99528 3.43 0.59
## [910,] 7.0 11.0 0.99331 3.29 0.52
## [911,] 3.0 11.0 0.99577 3.23 0.57
## [912,] 3.0 9.0 0.99901 3.18 0.60
## [913,] 4.0 8.0 0.99674 3.33 0.62
## [914,] 3.0 10.0 0.99639 3.27 0.64
## [915,] 7.0 11.0 0.99331 3.29 0.52
## [916,] 3.0 6.0 0.99512 3.27 0.67
## [917,] 7.0 62.0 0.99395 3.62 0.61
## [918,] 26.0 45.0 0.99824 3.38 0.64
## [919,] 32.0 79.0 0.99640 3.30 0.72
## [920,] 38.0 46.0 0.99504 3.38 0.89
## [921,] 10.0 23.0 0.99786 3.24 0.56
## [922,] 32.0 79.0 0.99640 3.30 0.72
## [923,] 38.0 46.0 0.99504 3.38 0.89
## [924,] 26.0 45.0 0.99824 3.38 0.64
## [925,] 14.0 28.0 0.99516 3.18 0.80
## [926,] 53.0 77.0 0.99604 3.47 0.87
## [927,] 52.0 73.0 0.99786 3.47 0.90
## [928,] 11.0 75.0 0.99736 3.20 0.59
## [929,] 14.0 28.0 0.99516 3.18 0.80
## [930,] 10.0 19.0 0.99468 3.30 0.73
## [931,] 8.0 25.0 0.99746 3.69 0.73
## [932,] 13.0 38.0 0.99748 3.48 0.65
## [933,] 29.0 66.0 0.99710 3.45 0.59
## [934,] 13.0 38.0 0.99748 3.48 0.65
## [935,] 8.0 25.0 0.99746 3.69 0.73
## [936,] 19.0 72.0 0.99543 3.39 0.72
## [937,] 19.0 72.0 0.99543 3.39 0.72
## [938,] 6.0 26.0 0.99791 3.07 0.60
## [939,] 23.0 42.0 0.99356 3.34 0.72
## [940,] 7.0 11.0 0.99425 3.61 0.54
## [941,] 13.0 25.0 0.99509 3.36 0.76
## [942,] 9.0 17.0 0.99484 3.19 0.52
## [943,] 13.0 52.0 0.99834 3.22 0.64
## [944,] 10.0 45.0 0.99864 3.24 0.60
## [945,] 11.0 24.0 0.99498 3.27 0.64
## [946,] 7.0 20.0 0.99566 3.14 0.79
## [947,] 11.0 24.0 0.99745 3.29 0.99
## [948,] 6.0 12.0 0.99408 3.26 0.62
## [949,] 10.0 21.0 0.99552 3.41 0.76
## [950,] 10.0 21.0 0.99552 3.41 0.76
## [951,] 10.0 21.0 0.99552 3.41 0.76
## [952,] 6.0 12.0 0.99408 3.26 0.62
## [953,] 6.0 10.0 0.99536 3.31 0.68
## [954,] 5.0 9.0 0.99458 3.20 0.69
## [955,] 6.0 11.0 0.99538 3.44 0.54
## [956,] 9.0 23.0 0.99648 3.36 0.67
## [957,] 5.0 10.0 0.99568 3.31 0.62
## [958,] 12.0 51.0 0.99613 3.29 0.58
## [959,] 25.0 36.0 0.99519 3.47 0.71
## [960,] 12.0 32.0 0.99735 3.36 0.61
## [961,] 18.0 38.0 0.99518 3.16 0.85
## [962,] 7.0 18.0 0.99592 3.27 0.62
## [963,] 6.0 16.0 0.99654 3.38 0.69
## [964,] 20.0 27.0 0.99546 3.15 0.69
## [965,] 18.0 38.0 0.99518 3.16 0.85
## [966,] 24.0 48.0 0.99554 3.34 0.86
## [967,] 6.0 10.0 0.99604 3.13 0.58
## [968,] 23.0 113.0 0.99733 3.13 0.48
## [969,] 29.0 46.0 0.99430 3.20 0.60
## [970,] 7.0 22.0 0.99669 3.46 0.61
## [971,] 6.0 10.0 0.99724 3.33 0.87
## [972,] 6.0 10.0 0.99724 3.33 0.87
## [973,] 6.0 13.0 0.99643 3.22 0.65
## [974,] 6.0 12.0 0.99605 3.52 0.63
## [975,] 7.0 13.0 0.99658 3.30 0.62
## [976,] 35.0 72.0 0.99700 3.44 0.52
## [977,] 35.0 72.0 0.99700 3.44 0.52
## [978,] 31.0 119.0 0.99801 3.15 0.50
## [979,] 9.0 29.0 0.99416 3.28 0.49
## [980,] 3.0 6.0 0.99690 3.13 0.63
## [981,] 8.0 17.0 0.99774 3.32 0.71
## [982,] 13.0 28.0 0.99712 3.25 0.62
## [983,] 51.0 70.0 0.99418 3.34 0.82
## [984,] 8.0 17.0 0.99774 3.32 0.71
## [985,] 3.0 6.0 0.99690 3.13 0.63
## [986,] 7.0 11.0 0.99562 3.45 0.58
## [987,] 3.0 7.0 0.99470 3.19 0.55
## [988,] 35.0 70.0 0.99693 3.44 0.50
## [989,] 19.0 27.0 0.99596 3.16 0.49
## [990,] 14.0 21.0 0.99556 3.14 0.51
## [991,] 19.0 27.0 0.99596 3.16 0.49
## [992,] 31.0 68.0 0.99694 3.45 0.48
## [993,] 30.0 47.0 0.99554 3.36 0.48
## [994,] 31.0 68.0 0.99694 3.45 0.48
## [995,] 20.0 88.0 0.99918 3.15 0.43
## [996,] 19.0 41.0 0.99697 3.39 0.62
## [997,] 3.0 11.0 0.99378 3.71 0.63
## [998,] 3.0 11.0 0.99378 3.71 0.63
## [999,] 4.0 10.0 0.99554 3.12 0.48
## [1000,] 7.0 12.0 0.99162 3.47 0.53
## [1001,] 6.0 12.0 0.99495 3.44 0.72
## [1002,] 31.0 47.0 0.99676 3.26 0.82
## [1003,] 13.0 27.0 0.99516 3.26 0.84
## [1004,] 4.0 8.0 0.99280 3.36 0.55
## [1005,] 27.0 45.0 0.99603 3.25 0.54
## [1006,] 4.0 8.0 0.99280 3.36 0.55
## [1007,] 13.0 27.0 0.99516 3.26 0.84
## [1008,] 12.0 25.0 0.99516 3.26 0.84
## [1009,] 12.0 24.0 0.99549 3.23 0.70
## [1010,] 26.0 55.0 0.99722 3.18 0.68
## [1011,] 7.0 12.0 0.99354 3.25 0.55
## [1012,] 12.0 19.0 0.99570 3.17 0.55
## [1013,] 11.0 32.0 0.99604 3.23 0.48
## [1014,] 22.0 31.0 0.99635 3.38 0.58
## [1015,] 3.0 7.0 0.99454 3.22 0.58
## [1016,] 14.0 27.0 0.99598 3.06 0.84
## [1017,] 12.0 28.0 0.99486 3.27 0.75
## [1018,] 36.0 109.0 0.99007 2.89 0.44
## [1019,] 36.0 109.0 0.99007 2.89 0.44
## [1020,] 10.0 23.0 0.99636 3.53 0.61
## [1021,] 3.0 8.0 0.99642 3.20 0.53
## [1022,] 3.0 8.0 0.99642 3.20 0.53
## [1023,] 4.0 9.0 0.99584 3.35 0.54
## [1024,] 3.0 9.0 0.99506 3.27 0.55
## [1025,] 12.0 18.0 0.99568 3.32 0.56
## [1026,] 17.0 43.0 0.99822 3.33 0.60
## [1027,] 14.0 36.0 0.99364 3.41 0.56
## [1028,] 28.0 52.0 0.99378 3.49 0.52
## [1029,] 41.0 68.0 0.99586 3.37 0.54
## [1030,] 12.0 18.0 0.99568 3.32 0.56
## [1031,] 9.0 14.0 0.99488 3.42 0.55
## [1032,] 9.0 15.0 0.99514 3.35 0.58
## [1033,] 5.0 14.0 0.99854 3.36 0.53
## [1034,] 14.0 27.0 0.99592 3.30 0.59
## [1035,] 15.0 48.0 0.99739 3.20 0.47
## [1036,] 5.0 17.0 0.99683 3.17 0.65
## [1037,] 26.0 35.0 0.99356 3.22 0.59
## [1038,] 20.0 56.0 0.99672 3.35 0.56
## [1039,] 25.0 42.0 0.99530 3.24 0.77
## [1040,] 21.0 39.0 0.99692 3.33 0.83
## [1041,] 16.0 32.0 0.99756 3.58 0.67
## [1042,] 13.0 26.0 0.99547 3.38 0.64
## [1043,] 21.0 39.0 0.99692 3.33 0.83
## [1044,] 18.0 39.0 0.99859 3.29 0.81
## [1045,] 12.0 53.0 0.99294 3.36 0.62
## [1046,] 32.0 38.0 0.99438 3.32 0.58
## [1047,] 33.0 45.0 0.99612 3.31 0.62
## [1048,] 27.0 51.0 0.99634 3.49 0.64
## [1049,] 3.0 14.0 0.99702 3.27 0.78
## [1050,] 6.0 17.0 0.99704 3.27 0.77
## [1051,] 27.0 51.0 0.99634 3.49 0.64
## [1052,] 16.0 45.0 0.99702 3.03 1.34
## [1053,] 19.0 25.0 0.99258 3.56 0.55
## [1054,] 9.0 20.0 0.99426 3.38 0.77
## [1055,] 5.0 77.0 0.99747 3.13 0.62
## [1056,] 5.0 77.0 0.99747 3.13 0.62
## [1057,] 3.0 10.0 0.99586 3.21 0.59
## [1058,] 28.0 90.0 0.99784 3.15 0.57
## [1059,] 30.0 52.0 0.99710 3.19 0.76
## [1060,] 3.0 10.0 0.99586 3.21 0.59
## [1061,] 3.0 8.0 0.99810 3.14 0.70
## [1062,] 7.0 16.0 0.99462 3.21 0.69
## [1063,] 6.0 15.0 0.99560 3.30 0.64
## [1064,] 6.0 17.0 0.99565 3.22 0.63
## [1065,] 5.0 10.0 0.99418 3.28 0.57
## [1066,] 6.0 20.0 0.99630 3.29 0.60
## [1067,] 13.0 26.0 0.99358 3.40 0.72
## [1068,] 3.0 10.0 0.99572 3.02 0.83
## [1069,] 3.0 10.0 0.99572 3.02 0.83
## [1070,] 28.0 52.0 0.99700 3.31 0.62
## [1071,] 19.0 37.0 0.99498 3.18 0.89
## [1072,] 30.0 92.0 0.99892 3.20 0.58
## [1073,] 33.0 75.0 0.99680 3.40 0.58
## [1074,] 16.0 37.0 0.99720 3.31 0.58
## [1075,] 30.0 92.0 0.99892 3.20 0.58
## [1076,] 45.0 67.0 0.99769 3.44 0.86
## [1077,] 3.0 8.0 0.99534 3.15 0.73
## [1078,] 3.0 8.0 0.99817 3.27 0.58
## [1079,] 3.0 8.0 0.99817 3.27 0.58
## [1080,] 37.5 278.0 0.99316 3.01 0.51
## [1081,] 3.0 8.0 0.99471 3.16 0.51
## [1082,] 37.5 289.0 0.99316 3.01 0.51
## [1083,] 31.0 70.0 0.99685 3.42 0.59
## [1084,] 32.0 59.0 0.99617 3.33 0.77
## [1085,] 31.0 70.0 0.99685 3.42 0.59
## [1086,] 40.0 64.0 0.99529 3.12 0.49
## [1087,] 3.0 9.0 0.99738 3.38 0.66
## [1088,] 18.0 30.0 0.99400 3.29 0.69
## [1089,] 22.0 41.0 0.99735 3.02 0.76
## [1090,] 22.0 41.0 0.99735 3.02 0.76
## [1091,] 42.0 74.0 0.99451 2.98 0.63
## [1092,] 8.0 19.0 0.99546 3.35 0.60
## [1093,] 10.0 16.0 0.99479 3.43 0.59
## [1094,] 11.0 31.0 0.99615 3.33 0.86
## [1095,] 9.0 17.0 0.99655 3.35 0.49
## [1096,] 6.0 20.0 0.99772 3.15 0.50
## [1097,] 9.0 17.0 0.99655 3.35 0.49
## [1098,] 5.0 18.0 0.99666 3.20 0.52
## [1099,] 5.0 16.0 0.99358 3.15 0.81
## [1100,] 5.0 18.0 0.99666 3.20 0.52
## [1101,] 23.0 36.0 0.99392 3.11 0.78
## [1102,] 14.0 25.0 0.99388 3.35 0.63
## [1103,] 18.0 30.0 0.99402 3.45 0.54
## [1104,] 14.0 25.0 0.99388 3.35 0.63
## [1105,] 16.0 25.0 0.99360 3.28 0.66
## [1106,] 13.0 49.0 0.99374 3.41 0.60
## [1107,] 9.0 17.0 0.99376 3.21 0.54
## [1108,] 10.0 24.0 0.99523 3.27 0.85
## [1109,] 4.0 13.0 0.99855 3.36 0.49
## [1110,] 27.0 66.0 0.99820 3.17 0.76
## [1111,] 6.0 14.0 0.99593 3.25 0.64
## [1112,] 23.0 55.0 0.99471 3.78 0.64
## [1113,] 6.0 35.0 0.99396 3.29 0.71
## [1114,] 3.0 10.0 0.99698 3.12 0.59
## [1115,] 29.0 80.0 0.99020 3.49 0.66
## [1116,] 15.0 21.0 0.99572 3.38 0.60
## [1117,] 15.0 21.0 0.99572 3.38 0.60
## [1118,] 15.0 21.0 0.99572 3.38 0.60
## [1119,] 14.0 24.0 0.99252 3.30 0.53
## [1120,] 7.0 15.0 0.99256 3.52 0.58
## [1121,] 8.0 17.0 0.99235 3.20 0.72
## [1122,] 21.0 28.0 0.99352 3.44 0.55
## [1123,] 27.0 33.0 0.99220 3.45 0.48
## [1124,] 17.0 29.0 0.99674 3.12 0.65
## [1125,] 3.0 13.0 0.99557 3.62 0.62
## [1126,] 13.0 27.0 0.99394 3.14 0.59
## [1127,] 3.0 11.0 0.99150 3.39 0.54
## [1128,] 26.0 52.0 0.99379 3.51 0.60
## [1129,] 28.0 89.0 0.99840 3.22 0.68
## [1130,] 24.0 70.0 0.99798 3.21 0.69
## [1131,] 5.0 10.0 0.99770 3.18 0.63
## [1132,] 57.0 135.0 0.99341 3.32 0.44
## [1133,] 18.0 38.0 0.99330 3.38 0.88
## [1134,] 10.0 18.0 0.99684 3.37 0.68
## [1135,] 6.0 15.0 0.99524 3.30 0.74
## [1136,] 22.0 50.0 0.99460 3.38 0.60
## [1137,] 6.0 13.0 0.99774 3.22 0.76
## [1138,] 6.0 13.0 0.99774 3.22 0.76
## [1139,] 29.0 94.0 0.99786 3.14 0.58
## [1140,] 16.0 86.0 0.99764 3.34 0.64
## [1141,] 33.0 79.0 0.99690 3.41 0.65
## [1142,] 24.0 71.0 0.99624 3.27 0.85
## [1143,] 6.0 12.0 0.99354 3.30 0.65
## [1144,] 16.0 20.0 0.99672 3.61 0.82
## [1145,] 35.0 70.0 0.99588 3.43 0.62
## [1146,] 31.0 51.0 0.99672 3.53 0.81
## [1147,] 6.0 21.0 0.99600 3.28 0.87
## [1148,] 6.0 14.0 0.99702 3.21 0.49
## [1149,] 9.0 21.0 0.99526 3.39 0.66
## [1150,] 6.0 11.0 0.99585 3.23 0.52
## [1151,] 4.0 12.0 0.99473 3.30 0.76
## [1152,] 16.0 70.0 0.99352 3.46 0.65
## [1153,] 9.0 38.0 0.99616 3.15 0.53
## [1154,] 17.0 38.0 0.99622 3.23 0.66
## [1155,] 50.0 63.0 0.99544 3.59 0.68
## [1156,] 9.0 38.0 0.99616 3.15 0.53
## [1157,] 45.0 88.0 0.99524 3.33 0.76
## [1158,] 16.0 101.0 0.99240 3.46 0.87
## [1159,] 22.0 54.0 0.99572 3.42 1.16
## [1160,] 11.0 37.0 0.99728 3.16 0.67
## [1161,] 6.0 20.0 0.99676 3.14 0.72
## [1162,] 12.0 21.0 0.99551 3.21 0.75
## [1163,] 12.0 19.0 0.99434 3.14 0.71
## [1164,] 10.0 21.0 0.99692 3.29 0.67
## [1165,] 10.0 21.0 0.99692 3.29 0.67
## [1166,] 15.0 38.0 0.99634 3.01 1.10
## [1167,] 7.0 60.0 0.99709 2.94 0.98
## [1168,] 29.0 48.0 0.99528 3.32 0.88
## [1169,] 8.0 44.0 0.99384 3.21 0.56
## [1170,] 5.0 14.0 0.99502 3.32 0.62
## [1171,] 5.0 12.0 0.99667 3.20 0.67
## [1172,] 26.0 44.0 0.99522 3.42 0.68
## [1173,] 5.0 16.0 0.99649 3.27 0.74
## [1174,] 26.0 65.0 0.99716 3.46 0.62
## [1175,] 26.0 65.0 0.99716 3.46 0.62
## [1176,] 48.0 59.0 0.99541 3.61 0.70
## [1177,] 23.0 47.0 0.99572 3.58 0.50
## [1178,] 6.0 11.0 0.99318 3.35 0.66
## [1179,] 17.0 78.0 0.99346 3.68 0.73
## [1180,] 11.0 47.0 0.99599 3.27 0.81
## [1181,] 11.0 47.0 0.99599 3.27 0.81
## [1182,] 5.0 11.0 0.99478 3.19 0.46
## [1183,] 41.0 54.0 0.99754 3.38 0.86
## [1184,] 16.0 61.0 0.99572 3.29 0.60
## [1185,] 11.0 119.0 0.99538 3.36 0.70
## [1186,] 6.0 39.0 0.99346 3.33 0.46
## [1187,] 6.0 12.0 0.99630 3.52 0.50
## [1188,] 6.0 39.0 0.99346 3.33 0.46
## [1189,] 11.0 119.0 0.99538 3.36 0.70
## [1190,] 5.0 19.0 0.99616 3.30 0.44
## [1191,] 6.0 20.0 0.99652 3.28 0.57
## [1192,] 6.0 12.0 0.99551 3.56 0.51
## [1193,] 11.0 41.0 0.99439 3.52 0.80
## [1194,] 6.0 12.0 0.99551 3.56 0.51
## [1195,] 15.0 64.0 0.99588 3.22 0.59
## [1196,] 21.0 56.0 0.99633 3.52 0.60
## [1197,] 23.0 94.0 0.99686 3.21 0.58
## [1198,] 4.0 9.0 0.99458 3.16 0.54
## [1199,] 19.0 77.0 0.99510 3.15 0.79
## [1200,] 23.0 94.0 0.99686 3.21 0.58
## [1201,] 4.0 9.0 0.99458 3.16 0.54
## [1202,] 5.0 10.0 0.99419 3.27 0.54
## [1203,] 6.0 12.0 0.99516 3.35 0.69
## [1204,] 33.0 76.0 0.99878 3.14 0.55
## [1205,] 24.0 44.0 0.99534 3.40 0.85
## [1206,] 24.0 44.0 0.99534 3.40 0.85
## [1207,] 24.0 44.0 0.99534 3.40 0.85
## [1208,] 24.0 52.0 0.99752 3.35 0.94
## [1209,] 24.0 44.0 0.99534 3.40 0.85
## [1210,] 14.0 24.0 0.99428 3.45 0.87
## [1211,] 8.0 15.0 0.99498 3.35 0.62
## [1212,] 22.0 53.0 0.99570 3.32 0.62
## [1213,] 8.0 15.0 0.99498 3.35 0.62
## [1214,] 5.0 13.0 0.99659 3.15 0.75
## [1215,] 6.0 9.0 0.99628 3.10 0.48
## [1216,] 20.0 29.0 0.99488 3.26 0.56
## [1217,] 10.0 79.0 0.99677 3.29 0.69
## [1218,] 43.0 74.0 0.99408 3.23 0.81
## [1219,] 8.0 24.0 0.99600 3.24 0.69
## [1220,] 25.0 50.0 0.99478 3.20 0.83
## [1221,] 17.0 44.0 0.99734 3.28 0.77
## [1222,] 17.0 44.0 0.99734 3.28 0.77
## [1223,] 33.0 89.0 0.99678 3.26 0.46
## [1224,] 9.0 23.0 0.99638 3.23 0.76
## [1225,] 8.0 20.0 0.99920 3.07 0.82
## [1226,] 18.0 77.0 0.99922 3.15 0.51
## [1227,] 27.0 46.0 0.99592 3.02 0.47
## [1228,] 8.0 21.0 0.99769 3.27 0.72
## [1229,] 18.0 88.0 0.99157 3.68 0.73
## [1230,] 19.0 66.0 0.99718 3.40 0.64
## [1231,] 15.0 58.0 0.99470 3.37 0.78
## [1232,] 48.0 90.0 0.99621 3.38 0.62
## [1233,] 19.0 66.0 0.99718 3.40 0.64
## [1234,] 14.0 36.0 0.99614 3.23 0.49
## [1235,] 11.0 18.0 0.99242 3.39 0.40
## [1236,] 6.0 113.0 0.99572 3.30 0.56
## [1237,] 7.0 17.0 0.99659 3.26 0.64
## [1238,] 11.0 18.0 0.99242 3.39 0.40
## [1239,] 12.0 24.0 0.99798 3.38 0.46
## [1240,] 11.0 20.0 0.99488 3.45 0.56
## [1241,] 36.0 60.0 0.99494 3.10 0.40
## [1242,] 28.0 65.0 0.99729 3.16 0.59
## [1243,] 15.0 40.0 0.99414 3.22 0.60
## [1244,] 10.0 86.0 0.99830 3.37 0.62
## [1245,] 72.0 160.0 0.99721 3.33 0.54
## [1246,] 15.0 34.0 0.99655 3.49 0.68
## [1247,] 15.0 48.0 0.99502 3.12 0.48
## [1248,] 15.0 34.0 0.99655 3.49 0.68
## [1249,] 22.0 36.0 0.99490 3.41 0.75
## [1250,] 24.0 37.0 0.99514 3.40 0.61
## [1251,] 24.0 37.0 0.99514 3.40 0.61
## [1252,] 27.0 60.0 0.99630 3.28 0.59
## [1253,] 6.0 14.0 0.99627 3.45 0.58
## [1254,] 6.0 13.0 0.99569 3.43 0.58
## [1255,] 20.0 35.0 0.99628 3.40 0.69
## [1256,] 26.0 46.0 0.99358 3.47 0.45
## [1257,] 43.0 60.0 0.99499 3.10 0.42
## [1258,] 12.0 69.0 0.99633 3.32 0.70
## [1259,] 15.0 33.0 0.99538 3.44 0.63
## [1260,] 15.0 33.0 0.99538 3.44 0.63
## [1261,] 19.0 56.0 0.99632 3.02 1.15
## [1262,] 17.0 24.0 0.99437 3.59 0.55
## [1263,] 34.0 66.0 0.99726 3.15 0.58
## [1264,] 13.0 26.0 0.99780 3.37 0.46
## [1265,] 26.0 51.0 0.99456 3.38 0.72
## [1266,] 16.0 36.0 0.99564 3.38 0.60
## [1267,] 16.0 36.0 0.99564 3.38 0.60
## [1268,] 13.0 19.0 0.99600 3.10 0.87
## [1269,] 21.0 51.0 0.99668 3.47 0.55
## [1270,] 28.0 87.0 0.99080 3.50 0.82
## [1271,] 26.0 60.0 0.99084 3.70 0.75
## [1272,] 24.0 64.0 0.99350 3.38 0.57
## [1273,] 25.0 44.0 0.99385 3.50 0.53
## [1274,] 34.0 44.0 0.99494 3.10 0.43
## [1275,] 17.0 36.0 0.99440 3.24 0.53
## [1276,] 13.0 81.0 0.99688 3.24 0.54
## [1277,] 3.0 10.0 0.99566 3.28 0.56
## [1278,] 3.0 10.0 0.99636 3.35 0.60
## [1279,] 13.0 81.0 0.99688 3.24 0.54
## [1280,] 3.0 9.0 0.99480 3.14 0.57
## [1281,] 28.0 54.0 0.99560 3.37 0.64
## [1282,] 28.0 54.0 0.99560 3.37 0.64
## [1283,] 9.0 22.0 0.99619 3.33 0.68
## [1284,] 17.0 69.0 0.99734 3.26 0.63
## [1285,] 18.0 36.0 0.99476 3.39 0.56
## [1286,] 20.0 47.0 0.99734 3.15 0.57
## [1287,] 17.0 31.0 0.99328 3.35 0.54
## [1288,] 3.0 7.0 0.99286 3.22 0.37
## [1289,] 20.0 110.0 0.99914 3.30 1.17
## [1290,] 20.0 110.0 0.99914 3.30 1.17
## [1291,] 6.0 12.0 0.99521 3.36 0.59
## [1292,] 25.0 60.0 0.99638 3.29 0.75
## [1293,] 14.0 28.0 0.99362 3.62 0.67
## [1294,] 6.0 12.0 0.99672 3.34 0.49
## [1295,] 25.0 60.0 0.99638 3.29 0.75
## [1296,] 51.0 77.5 0.99558 3.20 0.45
## [1297,] 51.0 77.5 0.99558 3.20 0.45
## [1298,] 15.0 29.0 0.99323 3.35 0.61
## [1299,] 11.0 18.0 0.99191 3.45 0.56
## [1300,] 5.0 9.0 0.99476 3.50 0.40
## [1301,] 15.0 28.0 0.99444 3.78 0.61
## [1302,] 20.0 57.0 0.99598 3.60 0.74
## [1303,] 4.0 15.0 0.99576 3.30 0.80
## [1304,] 28.0 68.0 0.99501 3.36 0.66
## [1305,] 21.0 68.0 0.99550 3.12 0.44
## [1306,] 24.0 88.0 0.99612 3.30 0.53
## [1307,] 22.0 91.0 0.99790 3.29 0.62
## [1308,] 15.0 29.0 0.99524 3.41 0.52
## [1309,] 22.0 91.0 0.99790 3.29 0.62
## [1310,] 27.0 63.0 0.99600 3.28 0.61
## [1311,] 24.0 88.0 0.99612 3.30 0.53
## [1312,] 12.0 27.0 0.99290 3.33 0.59
## [1313,] 14.0 41.0 0.99532 3.34 0.47
## [1314,] 20.0 65.0 0.99558 3.40 0.54
## [1315,] 24.0 69.0 0.99556 3.40 0.53
## [1316,] 17.0 91.0 0.99616 3.26 0.58
## [1317,] 16.0 46.0 0.99258 4.01 0.59
## [1318,] 10.0 41.0 0.99638 3.18 0.69
## [1319,] 17.0 91.0 0.99616 3.26 0.58
## [1320,] 18.0 64.0 0.99652 2.90 1.33
## [1321,] 12.0 88.0 0.99796 3.26 0.66
## [1322,] 16.0 46.0 0.99258 4.01 0.59
## [1323,] 9.0 18.0 0.99392 3.18 0.55
## [1324,] 21.0 55.0 0.99495 3.18 0.82
## [1325,] 18.0 34.0 0.99480 3.39 0.60
## [1326,] 18.0 34.0 0.99480 3.39 0.60
## [1327,] 18.0 34.0 0.99480 3.39 0.60
## [1328,] 18.0 34.0 0.99480 3.39 0.60
## [1329,] 13.0 38.0 0.99550 3.34 0.52
## [1330,] 17.0 91.0 0.99616 3.29 0.56
## [1331,] 17.0 91.0 0.99616 3.29 0.56
## [1332,] 31.0 67.0 0.99668 3.26 0.46
## [1333,] 6.0 25.0 0.99581 3.09 0.43
## [1334,] 12.0 48.0 0.99760 3.18 0.51
## [1335,] 4.0 11.0 0.99608 3.39 0.52
## [1336,] 19.0 40.0 0.99387 3.38 0.66
## [1337,] 15.0 26.0 0.99448 3.36 0.45
## [1338,] 15.0 26.0 0.99448 3.36 0.45
## [1339,] 15.0 26.0 0.99448 3.36 0.45
## [1340,] 13.0 31.0 0.99538 3.36 0.54
## [1341,] 13.0 31.0 0.99538 3.36 0.54
## [1342,] 13.0 31.0 0.99538 3.36 0.54
## [1343,] 17.0 31.0 0.99589 3.37 0.51
## [1344,] 13.0 31.0 0.99538 3.36 0.54
## [1345,] 8.0 20.0 0.99852 3.09 0.53
## [1346,] 10.0 28.0 0.99613 3.25 0.53
## [1347,] 5.0 13.0 0.99472 3.52 0.56
## [1348,] 7.0 12.0 0.99587 3.34 0.39
## [1349,] 7.0 12.0 0.99587 3.34 0.39
## [1350,] 21.0 41.0 0.99518 3.41 0.52
## [1351,] 32.0 73.0 0.99654 3.34 0.57
## [1352,] 8.0 46.0 0.99332 3.32 0.51
## [1353,] 14.0 57.0 0.99690 3.37 0.46
## [1354,] 14.0 57.0 0.99690 3.37 0.46
## [1355,] 7.0 28.0 0.99568 3.35 0.52
## [1356,] 5.0 32.0 0.99464 3.36 0.44
## [1357,] 4.0 28.0 0.99464 3.36 0.44
## [1358,] 27.0 67.0 0.99648 3.60 0.59
## [1359,] 52.0 98.0 0.99736 3.28 0.50
## [1360,] 6.0 28.0 0.99704 3.07 0.65
## [1361,] 11.0 38.0 0.99699 3.24 0.56
## [1362,] 13.0 54.0 0.99724 3.36 0.54
## [1363,] 6.0 28.0 0.99704 3.07 0.65
## [1364,] 11.0 63.0 0.99652 3.29 0.48
## [1365,] 10.0 31.0 0.99500 3.46 0.53
## [1366,] 10.0 42.0 0.99710 3.18 0.62
## [1367,] 10.0 45.0 0.99576 3.24 0.50
## [1368,] 9.0 105.0 0.99725 3.25 1.18
## [1369,] 16.0 74.0 0.99656 3.27 0.50
## [1370,] 4.0 8.0 0.99396 3.33 0.37
## [1371,] 12.0 66.0 0.99623 3.00 1.17
## [1372,] 5.0 14.0 0.99476 3.21 1.03
## [1373,] 12.0 66.0 0.99623 3.00 1.17
## [1374,] 34.0 89.0 0.99664 3.24 0.45
## [1375,] 16.0 29.0 0.99471 3.32 0.51
## [1376,] 13.0 100.0 0.99586 3.26 0.52
## [1377,] 7.0 31.0 0.99460 3.11 0.46
## [1378,] 23.0 74.0 0.99530 3.71 0.62
## [1379,] 10.0 28.0 0.99609 3.29 0.51
## [1380,] 11.0 35.0 0.99557 3.36 0.62
## [1381,] 11.0 35.0 0.99557 3.36 0.62
## [1382,] 4.0 25.0 0.99534 3.38 0.45
## [1383,] 25.0 105.0 0.99613 3.30 0.49
## [1384,] 25.0 105.0 0.99613 3.30 0.49
## [1385,] 20.0 84.0 0.99593 3.19 0.50
## [1386,] 34.0 85.0 0.99668 3.19 0.42
## [1387,] 8.0 23.0 0.99610 3.30 0.58
## [1388,] 8.0 23.0 0.99610 3.30 0.58
## [1389,] 7.0 49.0 0.99718 3.35 0.68
## [1390,] 36.0 111.0 0.99524 3.10 0.53
## [1391,] 15.0 33.0 0.99292 3.58 0.59
## [1392,] 5.0 33.0 0.99612 3.37 0.58
## [1393,] 5.0 12.0 0.99420 3.17 0.48
## [1394,] 19.0 59.0 0.99612 3.30 0.48
## [1395,] 27.0 73.0 0.99669 3.32 0.48
## [1396,] 3.0 12.0 0.99745 3.31 0.65
## [1397,] 4.0 11.0 0.99745 3.31 0.65
## [1398,] 17.0 104.0 0.99584 3.28 0.52
## [1399,] 13.0 28.0 0.99660 3.52 0.62
## [1400,] 10.0 24.0 0.99560 3.42 0.72
## [1401,] 33.0 141.0 0.99620 3.25 0.51
## [1402,] 33.0 141.0 0.99620 3.25 0.51
## [1403,] 6.0 24.0 0.99630 3.44 0.82
## [1404,] 3.0 13.0 0.99600 3.23 1.10
## [1405,] 12.0 46.0 0.99850 3.43 0.62
## [1406,] 18.0 34.0 0.99520 3.28 0.90
## [1407,] 8.0 22.0 0.99740 3.22 0.94
## [1408,] 40.0 54.0 0.99500 3.54 0.93
## [1409,] 35.0 53.0 0.99500 3.27 1.01
## [1410,] 40.0 54.0 0.99500 3.54 0.93
## [1411,] 5.0 16.0 0.99360 3.50 0.44
## [1412,] 8.0 19.0 0.99630 3.56 0.73
## [1413,] 8.0 22.0 0.99740 3.22 0.94
## [1414,] 12.0 89.0 0.99630 3.04 0.90
## [1415,] 3.0 15.0 0.99940 3.20 0.78
## [1416,] 8.0 18.0 0.99660 3.56 0.84
## [1417,] 3.0 15.0 0.99940 3.20 0.78
## [1418,] 21.0 37.0 0.99520 3.35 0.77
## [1419,] 3.0 19.0 0.99500 3.16 0.46
## [1420,] 32.0 133.0 0.99560 3.27 0.45
## [1421,] 3.0 19.0 0.99500 3.16 0.46
## [1422,] 24.0 58.0 0.99650 3.34 0.58
## [1423,] 39.0 55.0 0.99560 3.39 0.84
## [1424,] 14.0 31.0 0.99680 3.50 0.67
## [1425,] 8.0 23.0 0.99740 3.26 0.70
## [1426,] 8.0 23.0 0.99740 3.26 0.70
## [1427,] 23.0 60.0 0.99710 3.41 0.71
## [1428,] 6.0 23.0 0.99570 3.30 0.58
## [1429,] 27.0 55.0 0.99620 3.31 0.63
## [1430,] 38.0 67.0 0.99600 3.33 0.93
## [1431,] 18.0 47.0 0.99560 3.37 0.62
## [1432,] 13.0 74.0 0.99580 3.26 0.54
## [1433,] 3.0 11.0 0.99440 3.48 0.85
## [1434,] 11.0 25.0 0.99550 3.42 0.74
## [1435,] 55.0 95.0 1.00369 3.18 0.77
## [1436,] 55.0 95.0 1.00369 3.18 0.77
## [1437,] 27.0 90.0 0.99914 3.15 0.65
## [1438,] 15.0 39.0 0.99577 3.53 0.54
## [1439,] 3.0 8.0 0.99600 3.41 0.47
## [1440,] 31.0 92.0 0.99566 3.32 0.68
## [1441,] 15.0 28.0 0.99470 3.23 0.73
## [1442,] 19.0 98.0 0.99713 3.16 0.52
## [1443,] 18.0 30.0 0.99712 3.40 0.75
## [1444,] 8.0 22.0 0.99322 3.38 0.49
## [1445,] 31.0 92.0 0.99566 3.32 0.68
## [1446,] 19.0 98.0 0.99713 3.16 0.52
## [1447,] 18.0 30.0 0.99712 3.40 0.75
## [1448,] 22.0 39.0 0.99701 3.40 0.74
## [1449,] 40.0 54.0 0.99683 3.40 0.73
## [1450,] 15.0 29.0 0.99472 3.23 0.76
## [1451,] 15.0 28.0 0.99470 3.23 0.73
## [1452,] 8.0 17.0 0.99732 3.33 0.78
## [1453,] 37.0 53.0 0.99374 3.35 0.76
## [1454,] 27.0 85.0 0.99706 3.41 0.58
## [1455,] 7.0 23.0 0.99974 3.21 0.69
## [1456,] 9.0 17.0 0.99467 3.50 0.63
## [1457,] 38.0 89.0 0.99236 3.30 0.50
## [1458,] 27.0 85.0 0.99706 3.41 0.58
## [1459,] 8.0 20.0 0.99603 3.39 0.60
## [1460,] 7.0 15.0 0.99458 3.32 0.80
## [1461,] 34.0 64.0 0.99724 3.41 0.68
## [1462,] 6.0 13.0 0.99664 3.59 0.61
## [1463,] 14.0 31.0 0.99545 3.36 0.58
## [1464,] 14.0 39.0 0.99522 3.34 0.53
## [1465,] 34.0 53.0 0.99580 3.41 0.67
## [1466,] 34.0 53.0 0.99580 3.41 0.67
## [1467,] 31.0 54.0 0.99728 3.30 0.65
## [1468,] 19.0 32.0 0.99648 3.52 0.57
## [1469,] 31.0 54.0 0.99728 3.30 0.65
## [1470,] 20.0 49.0 0.99705 3.31 0.55
## [1471,] 8.0 24.0 0.99578 2.88 0.47
## [1472,] 8.0 16.0 0.99334 3.43 0.52
## [1473,] 23.0 44.0 0.99656 3.38 0.79
## [1474,] 14.0 45.0 0.99336 3.38 0.54
## [1475,] 48.0 82.0 1.00242 3.16 0.75
## [1476,] 16.0 89.0 0.99182 3.54 0.88
## [1477,] 48.0 82.0 1.00242 3.16 0.75
## [1478,] 16.0 89.0 0.99182 3.54 0.88
## [1479,] 3.0 14.0 0.99808 3.40 0.52
## [1480,] 10.0 22.0 0.99828 3.39 0.68
## [1481,] 6.0 13.0 0.99498 3.66 0.62
## [1482,] 10.0 22.0 0.99828 3.39 0.68
## [1483,] 3.0 9.0 0.99719 3.33 0.57
## [1484,] 6.0 12.0 0.99542 3.35 0.61
## [1485,] 4.0 11.0 0.99592 3.53 0.64
## [1486,] 8.0 25.0 0.99606 3.37 0.55
## [1487,] 9.0 23.0 0.99546 3.38 0.60
## [1488,] 9.0 17.0 0.99496 3.52 0.78
## [1489,] 5.0 13.0 0.99420 3.72 0.58
## [1490,] 4.0 11.0 0.99448 3.44 0.76
## [1491,] 8.0 18.0 0.99344 3.39 0.56
## [1492,] 5.0 13.0 0.99420 3.72 0.58
## [1493,] 6.0 18.0 0.99348 3.57 0.54
## [1494,] 23.0 147.0 0.99636 3.26 0.59
## [1495,] 15.0 28.0 0.99459 3.42 0.70
## [1496,] 15.0 28.0 0.99492 3.35 0.81
## [1497,] 23.0 147.0 0.99636 3.26 0.59
## [1498,] 7.0 16.0 0.99508 3.45 0.63
## [1499,] 7.0 13.0 0.99582 3.53 0.58
## [1500,] 7.0 16.0 0.99508 3.45 0.63
## [1501,] 8.0 15.0 0.99508 3.26 0.53
## [1502,] 21.0 64.0 0.99642 3.16 0.53
## [1503,] 15.0 60.0 0.99638 3.31 0.54
## [1504,] 6.0 14.0 0.99555 3.51 0.69
## [1505,] 5.0 12.0 0.99605 3.36 0.55
## [1506,] 6.0 12.0 0.99600 3.55 0.63
## [1507,] 6.0 14.0 0.99562 3.51 0.66
## [1508,] 5.0 12.0 0.99605 3.36 0.55
## [1509,] 17.0 25.0 0.99814 3.38 0.72
## [1510,] 7.0 20.0 0.99410 3.28 0.70
## [1511,] 26.0 48.0 0.99661 3.47 0.77
## [1512,] 19.0 27.0 0.99712 3.44 0.67
## [1513,] 11.0 17.0 0.99588 3.53 0.65
## [1514,] 17.0 65.0 0.99294 3.33 0.60
## [1515,] 16.0 65.0 0.99842 3.53 0.72
## [1516,] 16.0 65.0 0.99842 3.53 0.72
## [1517,] 23.0 58.0 0.99633 3.42 0.97
## [1518,] 29.0 44.0 0.99489 3.38 0.83
## [1519,] 7.0 20.0 0.99647 3.32 0.63
## [1520,] 14.0 27.0 0.99665 3.44 0.58
## [1521,] 29.0 44.0 0.99489 3.38 0.83
## [1522,] 9.0 23.0 0.99585 3.39 0.43
## [1523,] 23.0 58.0 0.99633 3.42 0.97
## [1524,] 29.0 61.0 0.99530 3.34 0.60
## [1525,] 22.0 47.0 0.99522 3.39 0.78
## [1526,] 18.0 34.0 0.99552 3.33 0.64
## [1527,] 18.0 38.0 0.99553 3.30 0.65
## [1528,] 15.0 24.0 0.99510 3.29 0.66
## [1529,] 21.0 59.0 0.99714 3.31 0.67
## [1530,] 42.0 52.0 0.99608 3.42 0.60
## [1531,] 9.0 18.0 0.99444 3.42 0.69
## [1532,] 13.0 28.0 0.99631 3.60 0.66
## [1533,] 18.0 22.0 0.99573 3.21 0.68
## [1534,] 32.0 84.0 0.99717 3.39 0.61
## [1535,] 13.0 29.0 0.99397 3.42 0.62
## [1536,] 15.0 35.0 0.99590 3.36 0.59
## [1537,] 24.0 45.0 0.99528 3.60 0.68
## [1538,] 20.0 31.0 0.99484 3.50 0.64
## [1539,] 15.0 26.0 0.99538 3.57 0.63
## [1540,] 34.0 60.0 0.99714 3.46 0.78
## [1541,] 11.0 32.0 0.99646 3.56 0.63
## [1542,] 19.0 49.0 0.99666 3.40 0.76
## [1543,] 29.0 38.0 0.99472 3.30 0.56
## [1544,] 14.0 19.0 0.99758 3.25 0.57
## [1545,] 12.0 19.0 0.99550 3.17 0.81
## [1546,] 16.0 28.0 0.99531 3.36 0.64
## [1547,] 17.0 26.0 0.99575 3.36 0.61
## [1548,] 12.0 26.0 0.99306 3.55 0.51
## [1549,] 19.0 50.0 0.99783 3.10 0.58
## [1550,] 17.0 24.0 0.99419 3.24 0.70
## [1551,] 17.0 26.0 0.99783 3.45 0.53
## [1552,] 18.0 27.0 0.99768 3.44 0.54
## [1553,] 32.0 54.0 0.99586 3.51 0.66
## [1554,] 18.0 28.0 0.99765 3.41 0.60
## [1555,] 15.0 23.0 0.99627 3.54 0.60
## [1556,] 15.0 24.0 0.99514 3.44 0.68
## [1557,] 12.0 20.0 0.99636 3.53 0.56
## [1558,] 15.0 23.0 0.99627 3.54 0.60
## [1559,] 66.0 115.0 0.99787 3.22 0.56
## [1560,] 31.0 131.0 0.99622 3.21 0.52
## [1561,] 31.0 131.0 0.99622 3.21 0.52
## [1562,] 31.0 131.0 0.99622 3.21 0.52
## [1563,] 12.0 20.0 0.99546 3.29 0.54
## [1564,] 12.0 20.0 0.99546 3.29 0.54
## [1565,] 12.0 20.0 0.99546 3.29 0.54
## [1566,] 26.0 42.0 0.99489 3.39 0.82
## [1567,] 24.0 52.0 0.99494 3.34 0.71
## [1568,] 12.0 20.0 0.99546 3.29 0.54
## [1569,] 25.0 42.0 0.99629 3.34 0.59
## [1570,] 15.0 34.0 0.99396 3.48 0.57
## [1571,] 19.0 35.0 0.99340 3.37 0.93
## [1572,] 15.0 25.0 0.99514 3.44 0.65
## [1573,] 35.0 104.0 0.99632 3.33 0.51
## [1574,] 15.0 50.0 0.99467 3.58 0.67
## [1575,] 23.0 92.0 0.99677 3.39 0.48
## [1576,] 12.0 20.0 0.99474 3.26 0.64
## [1577,] 16.0 29.0 0.99588 3.30 0.78
## [1578,] 13.0 27.0 0.99622 3.54 0.60
## [1579,] 13.0 20.0 0.99540 3.42 0.67
## [1580,] 24.0 32.0 0.99402 3.54 0.60
## [1581,] 9.0 26.0 0.99470 3.36 0.60
## [1582,] 24.0 32.0 0.99402 3.54 0.60
## [1583,] 13.0 27.0 0.99362 3.57 0.50
## [1584,] 32.0 98.0 0.99578 3.33 0.62
## [1585,] 24.0 34.0 0.99484 3.29 0.80
## [1586,] 22.0 48.0 0.99494 3.30 0.84
## [1587,] 34.0 60.0 0.99492 3.34 0.85
## [1588,] 18.0 28.0 0.99483 3.55 0.66
## [1589,] 34.0 102.0 0.99414 3.27 0.78
## [1590,] 29.0 79.0 0.99770 3.29 0.54
## [1591,] 26.0 35.0 0.99314 3.32 0.82
## [1592,] 16.0 26.0 0.99402 3.67 0.56
## [1593,] 29.0 40.0 0.99574 3.42 0.75
## [1594,] 28.0 38.0 0.99651 3.42 0.82
## [1595,] 32.0 44.0 0.99490 3.45 0.58
## [1596,] 39.0 51.0 0.99512 3.52 0.76
## [1597,] 29.0 40.0 0.99574 3.42 0.75
## [1598,] 32.0 44.0 0.99547 3.57 0.71
## [1599,] 18.0 42.0 0.99549 3.39 0.66
## alcohol
## [1,] 9.400000
## [2,] 9.800000
## [3,] 9.800000
## [4,] 9.800000
## [5,] 9.400000
## [6,] 9.400000
## [7,] 9.400000
## [8,] 10.000000
## [9,] 9.500000
## [10,] 10.500000
## [11,] 9.200000
## [12,] 10.500000
## [13,] 9.900000
## [14,] 9.100000
## [15,] 9.200000
## [16,] 9.200000
## [17,] 10.500000
## [18,] 9.300000
## [19,] 9.000000
## [20,] 9.200000
## [21,] 9.400000
## [22,] 9.700000
## [23,] 9.500000
## [24,] 9.400000
## [25,] 9.700000
## [26,] 9.300000
## [27,] 9.500000
## [28,] 9.500000
## [29,] 9.400000
## [30,] 9.800000
## [31,] 10.100000
## [32,] 10.600000
## [33,] 9.800000
## [34,] 9.400000
## [35,] 9.200000
## [36,] 9.600000
## [37,] 10.800000
## [38,] 9.700000
## [39,] 9.800000
## [40,] 10.500000
## [41,] 10.500000
## [42,] 9.300000
## [43,] 10.500000
## [44,] 10.300000
## [45,] 9.500000
## [46,] 13.100000
## [47,] 9.200000
## [48,] 9.500000
## [49,] 9.200000
## [50,] 9.200000
## [51,] 9.200000
## [52,] 9.400000
## [53,] 9.400000
## [54,] 9.400000
## [55,] 10.200000
## [56,] 9.500000
## [57,] 9.600000
## [58,] 9.400000
## [59,] 10.000000
## [60,] 9.400000
## [61,] 9.200000
## [62,] 9.300000
## [63,] 9.500000
## [64,] 9.800000
## [65,] 10.900000
## [66,] 10.900000
## [67,] 9.600000
## [68,] 10.700000
## [69,] 10.700000
## [70,] 10.500000
## [71,] 9.500000
## [72,] 9.500000
## [73,] 9.500000
## [74,] 9.200000
## [75,] 9.600000
## [76,] 10.500000
## [77,] 10.500000
## [78,] 10.700000
## [79,] 10.100000
## [80,] 9.100000
## [81,] 9.200000
## [82,] 9.400000
## [83,] 9.100000
## [84,] 9.400000
## [85,] 10.300000
## [86,] 10.100000
## [87,] 9.900000
## [88,] 9.600000
## [89,] 9.500000
## [90,] 9.000000
## [91,] 9.500000
## [92,] 9.900000
## [93,] 9.800000
## [94,] 9.600000
## [95,] 10.500000
## [96,] 12.900000
## [97,] 10.700000
## [98,] 9.200000
## [99,] 9.800000
## [100,] 9.000000
## [101,] 10.200000
## [102,] 10.400000
## [103,] 9.000000
## [104,] 9.200000
## [105,] 9.400000
## [106,] 9.200000
## [107,] 9.300000
## [108,] 9.300000
## [109,] 9.600000
## [110,] 9.300000
## [111,] 9.500000
## [112,] 9.800000
## [113,] 9.800000
## [114,] 9.700000
## [115,] 9.500000
## [116,] 10.500000
## [117,] 10.000000
## [118,] 9.400000
## [119,] 10.900000
## [120,] 9.200000
## [121,] 9.000000
## [122,] 10.900000
## [123,] 9.200000
## [124,] 9.500000
## [125,] 9.500000
## [126,] 9.400000
## [127,] 10.900000
## [128,] 10.900000
## [129,] 10.500000
## [130,] 9.400000
## [131,] 9.400000
## [132,] 13.000000
## [133,] 13.000000
## [134,] 9.800000
## [135,] 9.900000
## [136,] 9.600000
## [137,] 9.500000
## [138,] 9.200000
## [139,] 9.500000
## [140,] 9.500000
## [141,] 9.600000
## [142,] 9.500000
## [143,] 14.000000
## [144,] 9.400000
## [145,] 14.000000
## [146,] 9.400000
## [147,] 10.000000
## [148,] 9.300000
## [149,] 10.200000
## [150,] 10.500000
## [151,] 10.300000
## [152,] 9.400000
## [153,] 10.100000
## [154,] 10.100000
## [155,] 10.500000
## [156,] 10.500000
## [157,] 10.500000
## [158,] 10.500000
## [159,] 9.300000
## [160,] 9.300000
## [161,] 9.600000
## [162,] 9.200000
## [163,] 10.000000
## [164,] 9.400000
## [165,] 9.400000
## [166,] 9.500000
## [167,] 10.200000
## [168,] 9.000000
## [169,] 10.400000
## [170,] 9.500000
## [171,] 9.100000
## [172,] 9.200000
## [173,] 9.200000
## [174,] 11.500000
## [175,] 9.500000
## [176,] 9.500000
## [177,] 9.500000
## [178,] 10.500000
## [179,] 9.600000
## [180,] 9.500000
## [181,] 9.500000
## [182,] 9.300000
## [183,] 9.300000
## [184,] 9.300000
## [185,] 9.300000
## [186,] 9.700000
## [187,] 9.200000
## [188,] 9.700000
## [189,] 9.500000
## [190,] 9.500000
## [191,] 9.400000
## [192,] 9.800000
## [193,] 9.500000
## [194,] 9.700000
## [195,] 9.700000
## [196,] 9.400000
## [197,] 10.200000
## [198,] 10.100000
## [199,] 13.000000
## [200,] 11.400000
## [201,] 10.300000
## [202,] 9.300000
## [203,] 9.500000
## [204,] 9.200000
## [205,] 9.200000
## [206,] 10.800000
## [207,] 10.800000
## [208,] 9.300000
## [209,] 9.400000
## [210,] 10.500000
## [211,] 12.400000
## [212,] 10.000000
## [213,] 10.200000
## [214,] 10.100000
## [215,] 9.800000
## [216,] 10.500000
## [217,] 11.000000
## [218,] 9.100000
## [219,] 9.700000
## [220,] 9.500000
## [221,] 9.400000
## [222,] 9.400000
## [223,] 9.500000
## [224,] 10.000000
## [225,] 10.400000
## [226,] 10.500000
## [227,] 9.500000
## [228,] 9.800000
## [229,] 10.500000
## [230,] 11.000000
## [231,] 12.200000
## [232,] 9.900000
## [233,] 9.600000
## [234,] 11.000000
## [235,] 9.000000
## [236,] 9.000000
## [237,] 9.000000
## [238,] 9.200000
## [239,] 9.000000
## [240,] 9.000000
## [241,] 9.300000
## [242,] 10.900000
## [243,] 9.800000
## [244,] 9.200000
## [245,] 9.200000
## [246,] 9.900000
## [247,] 9.500000
## [248,] 9.300000
## [249,] 9.800000
## [250,] 9.900000
## [251,] 10.000000
## [252,] 9.900000
## [253,] 10.500000
## [254,] 9.500000
## [255,] 9.900000
## [256,] 9.300000
## [257,] 9.200000
## [258,] 9.200000
## [259,] 9.400000
## [260,] 10.500000
## [261,] 9.300000
## [262,] 9.400000
## [263,] 10.000000
## [264,] 9.300000
## [265,] 10.900000
## [266,] 10.200000
## [267,] 9.800000
## [268,] 12.800000
## [269,] 9.400000
## [270,] 10.100000
## [271,] 10.700000
## [272,] 10.100000
## [273,] 10.100000
## [274,] 9.400000
## [275,] 9.400000
## [276,] 10.700000
## [277,] 9.400000
## [278,] 10.100000
## [279,] 12.600000
## [280,] 10.500000
## [281,] 9.300000
## [282,] 9.900000
## [283,] 9.100000
## [284,] 10.500000
## [285,] 9.800000
## [286,] 9.800000
## [287,] 10.300000
## [288,] 10.300000
## [289,] 10.600000
## [290,] 9.200000
## [291,] 10.600000
## [292,] 10.500000
## [293,] 10.300000
## [294,] 10.100000
## [295,] 9.500000
## [296,] 9.500000
## [297,] 9.900000
## [298,] 9.600000
## [299,] 9.700000
## [300,] 9.600000
## [301,] 10.700000
## [302,] 10.100000
## [303,] 10.000000
## [304,] 9.500000
## [305,] 9.200000
## [306,] 9.300000
## [307,] 9.400000
## [308,] 9.500000
## [309,] 9.500000
## [310,] 9.500000
## [311,] 9.300000
## [312,] 9.400000
## [313,] 9.500000
## [314,] 9.400000
## [315,] 11.000000
## [316,] 11.000000
## [317,] 10.100000
## [318,] 10.400000
## [319,] 11.500000
## [320,] 10.400000
## [321,] 11.500000
## [322,] 9.700000
## [323,] 9.300000
## [324,] 9.500000
## [325,] 9.200000
## [326,] 9.200000
## [327,] 11.500000
## [328,] 11.500000
## [329,] 9.700000
## [330,] 9.500000
## [331,] 12.500000
## [332,] 12.500000
## [333,] 9.400000
## [334,] 11.000000
## [335,] 11.700000
## [336,] 12.200000
## [337,] 12.500000
## [338,] 10.300000
## [339,] 11.500000
## [340,] 9.800000
## [341,] 9.200000
## [342,] 11.300000
## [343,] 9.800000
## [344,] 9.800000
## [345,] 10.700000
## [346,] 9.900000
## [347,] 12.300000
## [348,] 12.000000
## [349,] 10.000000
## [350,] 9.400000
## [351,] 9.900000
## [352,] 9.400000
## [353,] 9.300000
## [354,] 13.000000
## [355,] 11.900000
## [356,] 12.800000
## [357,] 11.000000
## [358,] 11.700000
## [359,] 10.400000
## [360,] 9.800000
## [361,] 9.400000
## [362,] 9.900000
## [363,] 10.000000
## [364,] 10.200000
## [365,] 10.000000
## [366,] 11.800000
## [367,] 10.000000
## [368,] 9.000000
## [369,] 9.400000
## [370,] 12.000000
## [371,] 9.900000
## [372,] 8.700000
## [373,] 10.600000
## [374,] 9.200000
## [375,] 10.800000
## [376,] 11.800000
## [377,] 11.000000
## [378,] 12.000000
## [379,] 13.300000
## [380,] 10.800000
## [381,] 9.400000
## [382,] 10.000000
## [383,] 9.400000
## [384,] 9.400000
## [385,] 9.200000
## [386,] 9.700000
## [387,] 9.200000
## [388,] 9.600000
## [389,] 9.200000
## [390,] 10.000000
## [391,] 12.900000
## [392,] 10.000000
## [393,] 9.500000
## [394,] 9.100000
## [395,] 9.900000
## [396,] 13.000000
## [397,] 9.900000
## [398,] 11.000000
## [399,] 11.000000
## [400,] 9.400000
## [401,] 9.900000
## [402,] 10.800000
## [403,] 10.500000
## [404,] 10.500000
## [405,] 9.100000
## [406,] 10.100000
## [407,] 10.800000
## [408,] 10.800000
## [409,] 11.300000
## [410,] 9.600000
## [411,] 9.500000
## [412,] 9.500000
## [413,] 9.300000
## [414,] 11.700000
## [415,] 9.500000
## [416,] 9.300000
## [417,] 11.700000
## [418,] 10.500000
## [419,] 10.400000
## [420,] 9.900000
## [421,] 11.800000
## [422,] 12.300000
## [423,] 10.900000
## [424,] 11.000000
## [425,] 10.900000
## [426,] 12.300000
## [427,] 11.400000
## [428,] 10.600000
## [429,] 9.300000
## [430,] 10.400000
## [431,] 11.000000
## [432,] 9.200000
## [433,] 12.800000
## [434,] 9.500000
## [435,] 9.900000
## [436,] 9.500000
## [437,] 10.200000
## [438,] 11.200000
## [439,] 9.900000
## [440,] 9.300000
## [441,] 9.800000
## [442,] 11.300000
## [443,] 11.200000
## [444,] 11.600000
## [445,] 12.500000
## [446,] 10.100000
## [447,] 10.500000
## [448,] 11.200000
## [449,] 10.200000
## [450,] 10.800000
## [451,] 10.800000
## [452,] 9.100000
## [453,] 10.000000
## [454,] 11.200000
## [455,] 11.100000
## [456,] 13.400000
## [457,] 10.300000
## [458,] 9.600000
## [459,] 11.200000
## [460,] 9.000000
## [461,] 11.300000
## [462,] 9.300000
## [463,] 11.800000
## [464,] 9.000000
## [465,] 9.200000
## [466,] 9.700000
## [467,] 11.500000
## [468,] 14.000000
## [469,] 9.200000
## [470,] 9.800000
## [471,] 10.600000
## [472,] 11.400000
## [473,] 10.400000
## [474,] 10.600000
## [475,] 9.400000
## [476,] 10.200000
## [477,] 9.700000
## [478,] 11.000000
## [479,] 10.200000
## [480,] 10.100000
## [481,] 9.200000
## [482,] 11.700000
## [483,] 9.400000
## [484,] 9.400000
## [485,] 13.400000
## [486,] 10.000000
## [487,] 10.000000
## [488,] 10.000000
## [489,] 10.800000
## [490,] 10.200000
## [491,] 10.600000
## [492,] 13.300000
## [493,] 13.400000
## [494,] 11.600000
## [495,] 12.100000
## [496,] 11.000000
## [497,] 9.000000
## [498,] 11.100000
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## [1223,] 9.600000
## [1224,] 12.000000
## [1225,] 10.300000
## [1226,] 9.400000
## [1227,] 9.200000
## [1228,] 9.600000
## [1229,] 13.600000
## [1230,] 9.500000
## [1231,] 11.800000
## [1232,] 10.800000
## [1233,] 9.500000
## [1234,] 9.300000
## [1235,] 12.800000
## [1236,] 11.500000
## [1237,] 9.400000
## [1238,] 12.800000
## [1239,] 9.600000
## [1240,] 11.800000
## [1241,] 9.300000
## [1242,] 9.800000
## [1243,] 12.200000
## [1244,] 9.500000
## [1245,] 10.300000
## [1246,] 10.500000
## [1247,] 10.000000
## [1248,] 10.500000
## [1249,] 11.100000
## [1250,] 10.900000
## [1251,] 10.900000
## [1252,] 9.800000
## [1253,] 9.800000
## [1254,] 9.500000
## [1255,] 10.900000
## [1256,] 11.000000
## [1257,] 9.200000
## [1258,] 11.000000
## [1259,] 11.300000
## [1260,] 11.300000
## [1261,] 9.300000
## [1262,] 11.200000
## [1263,] 9.800000
## [1264,] 9.600000
## [1265,] 11.800000
## [1266,] 10.300000
## [1267,] 10.300000
## [1268,] 11.400000
## [1269,] 9.500000
## [1270,] 14.000000
## [1271,] 14.000000
## [1272,] 11.700000
## [1273,] 11.200000
## [1274,] 9.300000
## [1275,] 11.200000
## [1276,] 9.500000
## [1277,] 12.000000
## [1278,] 9.700000
## [1279,] 9.500000
## [1280,] 11.500000
## [1281,] 10.400000
## [1282,] 10.400000
## [1283,] 10.900000
## [1284,] 10.200000
## [1285,] 10.900000
## [1286,] 10.500000
## [1287,] 12.400000
## [1288,] 13.000000
## [1289,] 10.200000
## [1290,] 10.200000
## [1291,] 11.000000
## [1292,] 10.900000
## [1293,] 12.400000
## [1294,] 9.700000
## [1295,] 10.900000
## [1296,] 9.500000
## [1297,] 9.500000
## [1298,] 12.100000
## [1299,] 12.200000
## [1300,] 10.900000
## [1301,] 12.500000
## [1302,] 11.700000
## [1303,] 11.200000
## [1304,] 11.200000
## [1305,] 9.200000
## [1306,] 9.800000
## [1307,] 10.100000
## [1308,] 11.100000
## [1309,] 10.100000
## [1310,] 9.200000
## [1311,] 9.800000
## [1312,] 12.800000
## [1313,] 10.500000
## [1314,] 10.100000
## [1315,] 10.100000
## [1316,] 9.800000
## [1317,] 12.500000
## [1318,] 11.900000
## [1319,] 9.800000
## [1320,] 9.100000
## [1321,] 10.100000
## [1322,] 12.500000
## [1323,] 11.400000
## [1324,] 11.000000
## [1325,] 10.600000
## [1326,] 10.600000
## [1327,] 10.600000
## [1328,] 10.600000
## [1329,] 9.300000
## [1330,] 9.800000
## [1331,] 9.800000
## [1332,] 9.200000
## [1333,] 9.700000
## [1334,] 9.600000
## [1335,] 10.000000
## [1336,] 12.600000
## [1337,] 9.500000
## [1338,] 9.500000
## [1339,] 9.500000
## [1340,] 10.500000
## [1341,] 10.500000
## [1342,] 10.500000
## [1343,] 10.400000
## [1344,] 10.500000
## [1345,] 11.000000
## [1346,] 10.200000
## [1347,] 11.400000
## [1348,] 9.500000
## [1349,] 9.500000
## [1350,] 10.800000
## [1351,] 10.000000
## [1352,] 11.800000
## [1353,] 10.300000
## [1354,] 10.300000
## [1355,] 10.000000
## [1356,] 10.100000
## [1357,] 10.100000
## [1358,] 11.100000
## [1359,] 9.500000
## [1360,] 10.033333
## [1361,] 10.900000
## [1362,] 10.100000
## [1363,] 10.033333
## [1364,] 9.800000
## [1365,] 11.800000
## [1366,] 9.500000
## [1367,] 9.800000
## [1368,] 10.500000
## [1369,] 9.800000
## [1370,] 10.400000
## [1371,] 9.200000
## [1372,] 11.600000
## [1373,] 9.200000
## [1374,] 9.300000
## [1375,] 9.800000
## [1376,] 9.900000
## [1377,] 10.000000
## [1378,] 12.200000
## [1379,] 9.900000
## [1380,] 10.800000
## [1381,] 10.800000
## [1382,] 9.600000
## [1383,] 9.900000
## [1384,] 9.900000
## [1385,] 9.500000
## [1386,] 9.200000
## [1387,] 9.600000
## [1388,] 9.600000
## [1389,] 10.300000
## [1390,] 9.700000
## [1391,] 12.500000
## [1392,] 11.000000
## [1393,] 9.800000
## [1394,] 10.200000
## [1395,] 9.200000
## [1396,] 9.550000
## [1397,] 9.550000
## [1398,] 9.900000
## [1399,] 10.200000
## [1400,] 11.100000
## [1401,] 9.900000
## [1402,] 9.900000
## [1403,] 11.900000
## [1404,] 10.000000
## [1405,] 10.700000
## [1406,] 11.300000
## [1407,] 10.900000
## [1408,] 10.700000
## [1409,] 12.400000
## [1410,] 10.700000
## [1411,] 11.900000
## [1412,] 10.600000
## [1413,] 10.900000
## [1414,] 10.100000
## [1415,] 9.600000
## [1416,] 9.400000
## [1417,] 9.600000
## [1418,] 12.100000
## [1419,] 9.800000
## [1420,] 9.900000
## [1421,] 9.800000
## [1422,] 9.400000
## [1423,] 11.400000
## [1424,] 11.000000
## [1425,] 9.600000
## [1426,] 9.600000
## [1427,] 12.100000
## [1428,] 11.200000
## [1429,] 11.000000
## [1430,] 11.300000
## [1431,] 10.400000
## [1432,] 9.900000
## [1433,] 11.700000
## [1434,] 10.100000
## [1435,] 9.000000
## [1436,] 9.000000
## [1437,] 8.500000
## [1438,] 11.100000
## [1439,] 10.300000
## [1440,] 11.066667
## [1441,] 11.300000
## [1442,] 9.566667
## [1443,] 9.800000
## [1444,] 11.700000
## [1445,] 11.100000
## [1446,] 9.600000
## [1447,] 9.800000
## [1448,] 9.700000
## [1449,] 9.700000
## [1450,] 11.300000
## [1451,] 11.300000
## [1452,] 11.000000
## [1453,] 11.600000
## [1454,] 9.000000
## [1455,] 10.900000
## [1456,] 10.900000
## [1457,] 10.550000
## [1458,] 9.000000
## [1459,] 10.500000
## [1460,] 11.900000
## [1461,] 10.400000
## [1462,] 10.000000
## [1463,] 10.400000
## [1464,] 10.800000
## [1465,] 9.700000
## [1466,] 9.700000
## [1467,] 10.000000
## [1468,] 11.000000
## [1469,] 10.000000
## [1470,] 9.700000
## [1471,] 9.700000
## [1472,] 12.600000
## [1473,] 11.100000
## [1474,] 11.000000
## [1475,] 8.800000
## [1476,] 13.566667
## [1477,] 8.800000
## [1478,] 13.600000
## [1479,] 10.200000
## [1480,] 10.600000
## [1481,] 10.100000
## [1482,] 10.600000
## [1483,] 10.300000
## [1484,] 10.700000
## [1485,] 10.900000
## [1486,] 9.700000
## [1487,] 10.300000
## [1488,] 10.600000
## [1489,] 11.400000
## [1490,] 10.800000
## [1491,] 12.400000
## [1492,] 11.400000
## [1493,] 11.950000
## [1494,] 9.700000
## [1495,] 10.000000
## [1496,] 10.600000
## [1497,] 9.700000
## [1498,] 11.500000
## [1499,] 10.800000
## [1500,] 11.500000
## [1501,] 9.600000
## [1502,] 9.400000
## [1503,] 9.800000
## [1504,] 11.000000
## [1505,] 11.400000
## [1506,] 9.950000
## [1507,] 10.800000
## [1508,] 11.400000
## [1509,] 10.600000
## [1510,] 11.100000
## [1511,] 9.700000
## [1512,] 9.800000
## [1513,] 10.400000
## [1514,] 10.500000
## [1515,] 9.233333
## [1516,] 9.250000
## [1517,] 10.600000
## [1518,] 10.300000
## [1519,] 10.500000
## [1520,] 10.200000
## [1521,] 10.300000
## [1522,] 9.050000
## [1523,] 10.600000
## [1524,] 10.400000
## [1525,] 10.000000
## [1526,] 9.700000
## [1527,] 9.600000
## [1528,] 10.800000
## [1529,] 10.100000
## [1530,] 10.200000
## [1531,] 11.300000
## [1532,] 10.200000
## [1533,] 9.900000
## [1534,] 9.000000
## [1535,] 11.700000
## [1536,] 9.700000
## [1537,] 10.300000
## [1538,] 10.200000
## [1539,] 12.000000
## [1540,] 9.900000
## [1541,] 11.600000
## [1542,] 10.900000
## [1543,] 10.750000
## [1544,] 10.400000
## [1545,] 11.200000
## [1546,] 10.100000
## [1547,] 10.200000
## [1548,] 12.100000
## [1549,] 10.400000
## [1550,] 11.400000
## [1551,] 9.500000
## [1552,] 9.400000
## [1553,] 11.300000
## [1554,] 9.400000
## [1555,] 11.000000
## [1556,] 10.550000
## [1557,] 9.900000
## [1558,] 11.000000
## [1559,] 9.500000
## [1560,] 9.900000
## [1561,] 9.900000
## [1562,] 9.900000
## [1563,] 10.100000
## [1564,] 10.100000
## [1565,] 10.100000
## [1566,] 10.900000
## [1567,] 11.200000
## [1568,] 10.100000
## [1569,] 9.200000
## [1570,] 11.500000
## [1571,] 12.400000
## [1572,] 11.100000
## [1573,] 9.500000
## [1574,] 12.500000
## [1575,] 10.500000
## [1576,] 11.800000
## [1577,] 10.800000
## [1578,] 11.900000
## [1579,] 11.300000
## [1580,] 11.300000
## [1581,] 11.900000
## [1582,] 11.300000
## [1583,] 11.900000
## [1584,] 9.800000
## [1585,] 11.600000
## [1586,] 11.500000
## [1587,] 11.400000
## [1588,] 10.900000
## [1589,] 12.800000
## [1590,] 9.200000
## [1591,] 11.600000
## [1592,] 11.600000
## [1593,] 11.000000
## [1594,] 9.500000
## [1595,] 10.500000
## [1596,] 11.200000
## [1597,] 11.000000
## [1598,] 10.200000
## [1599,] 11.000000df_new <- cbind(df1, as.numeric(as.factor(df$quality))-1)df_new## fixed acidity volatile acidity citric acid residual sugar chlorides
## [1,] 7.4 0.700 0.00 1.90 0.076
## [2,] 7.8 0.880 0.00 2.60 0.098
## [3,] 7.8 0.760 0.04 2.30 0.092
## [4,] 11.2 0.280 0.56 1.90 0.075
## [5,] 7.4 0.700 0.00 1.90 0.076
## [6,] 7.4 0.660 0.00 1.80 0.075
## [7,] 7.9 0.600 0.06 1.60 0.069
## [8,] 7.3 0.650 0.00 1.20 0.065
## [9,] 7.8 0.580 0.02 2.00 0.073
## [10,] 7.5 0.500 0.36 6.10 0.071
## [11,] 6.7 0.580 0.08 1.80 0.097
## [12,] 7.5 0.500 0.36 6.10 0.071
## [13,] 5.6 0.615 0.00 1.60 0.089
## [14,] 7.8 0.610 0.29 1.60 0.114
## [15,] 8.9 0.620 0.18 3.80 0.176
## [16,] 8.9 0.620 0.19 3.90 0.170
## [17,] 8.5 0.280 0.56 1.80 0.092
## [18,] 8.1 0.560 0.28 1.70 0.368
## [19,] 7.4 0.590 0.08 4.40 0.086
## [20,] 7.9 0.320 0.51 1.80 0.341
## [21,] 8.9 0.220 0.48 1.80 0.077
## [22,] 7.6 0.390 0.31 2.30 0.082
## [23,] 7.9 0.430 0.21 1.60 0.106
## [24,] 8.5 0.490 0.11 2.30 0.084
## [25,] 6.9 0.400 0.14 2.40 0.085
## [26,] 6.3 0.390 0.16 1.40 0.080
## [27,] 7.6 0.410 0.24 1.80 0.080
## [28,] 7.9 0.430 0.21 1.60 0.106
## [29,] 7.1 0.710 0.00 1.90 0.080
## [30,] 7.8 0.645 0.00 2.00 0.082
## [31,] 6.7 0.675 0.07 2.40 0.089
## [32,] 6.9 0.685 0.00 2.50 0.105
## [33,] 8.3 0.655 0.12 2.30 0.083
## [34,] 6.9 0.605 0.12 10.70 0.073
## [35,] 5.2 0.320 0.25 1.80 0.103
## [36,] 7.8 0.645 0.00 5.50 0.086
## [37,] 7.8 0.600 0.14 2.40 0.086
## [38,] 8.1 0.380 0.28 2.10 0.066
## [39,] 5.7 1.130 0.09 1.50 0.172
## [40,] 7.3 0.450 0.36 5.90 0.074
## [41,] 7.3 0.450 0.36 5.90 0.074
## [42,] 8.8 0.610 0.30 2.80 0.088
## [43,] 7.5 0.490 0.20 2.60 0.332
## [44,] 8.1 0.660 0.22 2.20 0.069
## [45,] 6.8 0.670 0.02 1.80 0.050
## [46,] 4.6 0.520 0.15 2.10 0.054
## [47,] 7.7 0.935 0.43 2.20 0.114
## [48,] 8.7 0.290 0.52 1.60 0.113
## [49,] 6.4 0.400 0.23 1.60 0.066
## [50,] 5.6 0.310 0.37 1.40 0.074
## [51,] 8.8 0.660 0.26 1.70 0.074
## [52,] 6.6 0.520 0.04 2.20 0.069
## [53,] 6.6 0.500 0.04 2.10 0.068
## [54,] 8.6 0.380 0.36 3.00 0.081
## [55,] 7.6 0.510 0.15 2.80 0.110
## [56,] 7.7 0.620 0.04 3.80 0.084
## [57,] 10.2 0.420 0.57 3.40 0.070
## [58,] 7.5 0.630 0.12 5.10 0.111
## [59,] 7.8 0.590 0.18 2.30 0.076
## [60,] 7.3 0.390 0.31 2.40 0.074
## [61,] 8.8 0.400 0.40 2.20 0.079
## [62,] 7.7 0.690 0.49 1.80 0.115
## [63,] 7.5 0.520 0.16 1.90 0.085
## [64,] 7.0 0.735 0.05 2.00 0.081
## [65,] 7.2 0.725 0.05 4.65 0.086
## [66,] 7.2 0.725 0.05 4.65 0.086
## [67,] 7.5 0.520 0.11 1.50 0.079
## [68,] 6.6 0.705 0.07 1.60 0.076
## [69,] 9.3 0.320 0.57 2.00 0.074
## [70,] 8.0 0.705 0.05 1.90 0.074
## [71,] 7.7 0.630 0.08 1.90 0.076
## [72,] 7.7 0.670 0.23 2.10 0.088
## [73,] 7.7 0.690 0.22 1.90 0.084
## [74,] 8.3 0.675 0.26 2.10 0.084
## [75,] 9.7 0.320 0.54 2.50 0.094
## [76,] 8.8 0.410 0.64 2.20 0.093
## [77,] 8.8 0.410 0.64 2.20 0.093
## [78,] 6.8 0.785 0.00 2.40 0.104
## [79,] 6.7 0.750 0.12 2.00 0.086
## [80,] 8.3 0.625 0.20 1.50 0.080
## [81,] 6.2 0.450 0.20 1.60 0.069
## [82,] 7.8 0.430 0.70 1.90 0.464
## [83,] 7.4 0.500 0.47 2.00 0.086
## [84,] 7.3 0.670 0.26 1.80 0.401
## [85,] 6.3 0.300 0.48 1.80 0.069
## [86,] 6.9 0.550 0.15 2.20 0.076
## [87,] 8.6 0.490 0.28 1.90 0.110
## [88,] 7.7 0.490 0.26 1.90 0.062
## [89,] 9.3 0.390 0.44 2.10 0.107
## [90,] 7.0 0.620 0.08 1.80 0.076
## [91,] 7.9 0.520 0.26 1.90 0.079
## [92,] 8.6 0.490 0.28 1.90 0.110
## [93,] 8.6 0.490 0.29 2.00 0.110
## [94,] 7.7 0.490 0.26 1.90 0.062
## [95,] 5.0 1.020 0.04 1.40 0.045
## [96,] 4.7 0.600 0.17 2.30 0.058
## [97,] 6.8 0.775 0.00 3.00 0.102
## [98,] 7.0 0.500 0.25 2.00 0.070
## [99,] 7.6 0.900 0.06 2.50 0.079
## [100,] 8.1 0.545 0.18 1.90 0.080
## [101,] 8.3 0.610 0.30 2.10 0.084
## [102,] 7.8 0.500 0.30 1.90 0.075
## [103,] 8.1 0.545 0.18 1.90 0.080
## [104,] 8.1 0.575 0.22 2.10 0.077
## [105,] 7.2 0.490 0.24 2.20 0.070
## [106,] 8.1 0.575 0.22 2.10 0.077
## [107,] 7.8 0.410 0.68 1.70 0.467
## [108,] 6.2 0.630 0.31 1.70 0.088
## [109,] 8.0 0.330 0.53 2.50 0.091
## [110,] 8.1 0.785 0.52 2.00 0.122
## [111,] 7.8 0.560 0.19 1.80 0.104
## [112,] 8.4 0.620 0.09 2.20 0.084
## [113,] 8.4 0.600 0.10 2.20 0.085
## [114,] 10.1 0.310 0.44 2.30 0.080
## [115,] 7.8 0.560 0.19 1.80 0.104
## [116,] 9.4 0.400 0.31 2.20 0.090
## [117,] 8.3 0.540 0.28 1.90 0.077
## [118,] 7.8 0.560 0.12 2.00 0.082
## [119,] 8.8 0.550 0.04 2.20 0.119
## [120,] 7.0 0.690 0.08 1.80 0.097
## [121,] 7.3 1.070 0.09 1.70 0.178
## [122,] 8.8 0.550 0.04 2.20 0.119
## [123,] 7.3 0.695 0.00 2.50 0.075
## [124,] 8.0 0.710 0.00 2.60 0.080
## [125,] 7.8 0.500 0.17 1.60 0.082
## [126,] 9.0 0.620 0.04 1.90 0.146
## [127,] 8.2 1.330 0.00 1.70 0.081
## [128,] 8.1 1.330 0.00 1.80 0.082
## [129,] 8.0 0.590 0.16 1.80 0.065
## [130,] 6.1 0.380 0.15 1.80 0.072
## [131,] 8.0 0.745 0.56 2.00 0.118
## [132,] 5.6 0.500 0.09 2.30 0.049
## [133,] 5.6 0.500 0.09 2.30 0.049
## [134,] 6.6 0.500 0.01 1.50 0.060
## [135,] 7.9 1.040 0.05 2.20 0.084
## [136,] 8.4 0.745 0.11 1.90 0.090
## [137,] 8.3 0.715 0.15 1.80 0.089
## [138,] 7.2 0.415 0.36 2.00 0.081
## [139,] 7.8 0.560 0.19 2.10 0.081
## [140,] 7.8 0.560 0.19 2.00 0.081
## [141,] 8.4 0.745 0.11 1.90 0.090
## [142,] 8.3 0.715 0.15 1.80 0.089
## [143,] 5.2 0.340 0.00 1.80 0.050
## [144,] 6.3 0.390 0.08 1.70 0.066
## [145,] 5.2 0.340 0.00 1.80 0.050
## [146,] 8.1 0.670 0.55 1.80 0.117
## [147,] 5.8 0.680 0.02 1.80 0.087
## [148,] 7.6 0.490 0.26 1.60 0.236
## [149,] 6.9 0.490 0.10 2.30 0.074
## [150,] 8.2 0.400 0.44 2.80 0.089
## [151,] 7.3 0.330 0.47 2.10 0.077
## [152,] 9.2 0.520 1.00 3.40 0.610
## [153,] 7.5 0.600 0.03 1.80 0.095
## [154,] 7.5 0.600 0.03 1.80 0.095
## [155,] 7.1 0.430 0.42 5.50 0.070
## [156,] 7.1 0.430 0.42 5.50 0.071
## [157,] 7.1 0.430 0.42 5.50 0.070
## [158,] 7.1 0.430 0.42 5.50 0.071
## [159,] 7.1 0.680 0.00 2.20 0.073
## [160,] 6.8 0.600 0.18 1.90 0.079
## [161,] 7.6 0.950 0.03 2.00 0.090
## [162,] 7.6 0.680 0.02 1.30 0.072
## [163,] 7.8 0.530 0.04 1.70 0.076
## [164,] 7.4 0.600 0.26 7.30 0.070
## [165,] 7.3 0.590 0.26 7.20 0.070
## [166,] 7.8 0.630 0.48 1.70 0.100
## [167,] 6.8 0.640 0.10 2.10 0.085
## [168,] 7.3 0.550 0.03 1.60 0.072
## [169,] 6.8 0.630 0.07 2.10 0.089
## [170,] 7.5 0.705 0.24 1.80 0.360
## [171,] 7.9 0.885 0.03 1.80 0.058
## [172,] 8.0 0.420 0.17 2.00 0.073
## [173,] 8.0 0.420 0.17 2.00 0.073
## [174,] 7.4 0.620 0.05 1.90 0.068
## [175,] 7.3 0.380 0.21 2.00 0.080
## [176,] 6.9 0.500 0.04 1.50 0.085
## [177,] 7.3 0.380 0.21 2.00 0.080
## [178,] 7.5 0.520 0.42 2.30 0.087
## [179,] 7.0 0.805 0.00 2.50 0.068
## [180,] 8.8 0.610 0.14 2.40 0.067
## [181,] 8.8 0.610 0.14 2.40 0.067
## [182,] 8.9 0.610 0.49 2.00 0.270
## [183,] 7.2 0.730 0.02 2.50 0.076
## [184,] 6.8 0.610 0.20 1.80 0.077
## [185,] 6.7 0.620 0.21 1.90 0.079
## [186,] 8.9 0.310 0.57 2.00 0.111
## [187,] 7.4 0.390 0.48 2.00 0.082
## [188,] 7.7 0.705 0.10 2.60 0.084
## [189,] 7.9 0.500 0.33 2.00 0.084
## [190,] 7.9 0.490 0.32 1.90 0.082
## [191,] 8.2 0.500 0.35 2.90 0.077
## [192,] 6.4 0.370 0.25 1.90 0.074
## [193,] 6.8 0.630 0.12 3.80 0.099
## [194,] 7.6 0.550 0.21 2.20 0.071
## [195,] 7.6 0.550 0.21 2.20 0.071
## [196,] 7.8 0.590 0.33 2.00 0.074
## [197,] 7.3 0.580 0.30 2.40 0.074
## [198,] 11.5 0.300 0.60 2.00 0.067
## [199,] 5.4 0.835 0.08 1.20 0.046
## [200,] 6.9 1.090 0.06 2.10 0.061
## [201,] 9.6 0.320 0.47 1.40 0.056
## [202,] 8.8 0.370 0.48 2.10 0.097
## [203,] 6.8 0.500 0.11 1.50 0.075
## [204,] 7.0 0.420 0.35 1.60 0.088
## [205,] 7.0 0.430 0.36 1.60 0.089
## [206,] 12.8 0.300 0.74 2.60 0.095
## [207,] 12.8 0.300 0.74 2.60 0.095
## [208,] 7.8 0.570 0.31 1.80 0.069
## [209,] 7.8 0.440 0.28 2.70 0.100
## [210,] 11.0 0.300 0.58 2.10 0.054
## [211,] 9.7 0.530 0.60 2.00 0.039
## [212,] 8.0 0.725 0.24 2.80 0.083
## [213,] 11.6 0.440 0.64 2.10 0.059
## [214,] 8.2 0.570 0.26 2.20 0.060
## [215,] 7.8 0.735 0.08 2.40 0.092
## [216,] 7.0 0.490 0.49 5.60 0.060
## [217,] 8.7 0.625 0.16 2.00 0.101
## [218,] 8.1 0.725 0.22 2.20 0.072
## [219,] 7.5 0.490 0.19 1.90 0.076
## [220,] 7.8 0.530 0.33 2.40 0.080
## [221,] 7.8 0.340 0.37 2.00 0.082
## [222,] 7.4 0.530 0.26 2.00 0.101
## [223,] 6.8 0.610 0.04 1.50 0.057
## [224,] 8.6 0.645 0.25 2.00 0.083
## [225,] 8.4 0.635 0.36 2.00 0.089
## [226,] 7.7 0.430 0.25 2.60 0.073
## [227,] 8.9 0.590 0.50 2.00 0.337
## [228,] 9.0 0.820 0.14 2.60 0.089
## [229,] 7.7 0.430 0.25 2.60 0.073
## [230,] 6.9 0.520 0.25 2.60 0.081
## [231,] 5.2 0.480 0.04 1.60 0.054
## [232,] 8.0 0.380 0.06 1.80 0.078
## [233,] 8.5 0.370 0.20 2.80 0.090
## [234,] 6.9 0.520 0.25 2.60 0.081
## [235,] 8.2 1.000 0.09 2.30 0.065
## [236,] 7.2 0.630 0.00 1.90 0.097
## [237,] 7.2 0.630 0.00 1.90 0.097
## [238,] 7.2 0.645 0.00 1.90 0.097
## [239,] 7.2 0.630 0.00 1.90 0.097
## [240,] 8.2 1.000 0.09 2.30 0.065
## [241,] 8.9 0.635 0.37 1.70 0.263
## [242,] 12.0 0.380 0.56 2.10 0.093
## [243,] 7.7 0.580 0.10 1.80 0.102
## [244,] 15.0 0.210 0.44 2.20 0.075
## [245,] 15.0 0.210 0.44 2.20 0.075
## [246,] 7.3 0.660 0.00 2.00 0.084
## [247,] 7.1 0.680 0.07 1.90 0.075
## [248,] 8.2 0.600 0.17 2.30 0.072
## [249,] 7.7 0.530 0.06 1.70 0.074
## [250,] 7.3 0.660 0.00 2.00 0.084
## [251,] 10.8 0.320 0.44 1.60 0.063
## [252,] 7.1 0.600 0.00 1.80 0.074
## [253,] 11.1 0.350 0.48 3.10 0.090
## [254,] 7.7 0.775 0.42 1.90 0.092
## [255,] 7.1 0.600 0.00 1.80 0.074
## [256,] 8.0 0.570 0.23 3.20 0.073
## [257,] 9.4 0.340 0.37 2.20 0.075
## [258,] 6.6 0.695 0.00 2.10 0.075
## [259,] 7.7 0.410 0.76 1.80 0.611
## [260,] 10.0 0.310 0.47 2.60 0.085
## [261,] 7.9 0.330 0.23 1.70 0.077
## [262,] 7.0 0.975 0.04 2.00 0.087
## [263,] 8.0 0.520 0.03 1.70 0.070
## [264,] 7.9 0.370 0.23 1.80 0.077
## [265,] 12.5 0.560 0.49 2.40 0.064
## [266,] 11.8 0.260 0.52 1.80 0.071
## [267,] 8.1 0.870 0.00 3.30 0.096
## [268,] 7.9 0.350 0.46 3.60 0.078
## [269,] 6.9 0.540 0.04 3.00 0.077
## [270,] 11.5 0.180 0.51 4.00 0.104
## [271,] 7.9 0.545 0.06 4.00 0.087
## [272,] 11.5 0.180 0.51 4.00 0.104
## [273,] 10.9 0.370 0.58 4.00 0.071
## [274,] 8.4 0.715 0.20 2.40 0.076
## [275,] 7.5 0.650 0.18 7.00 0.088
## [276,] 7.9 0.545 0.06 4.00 0.087
## [277,] 6.9 0.540 0.04 3.00 0.077
## [278,] 11.5 0.180 0.51 4.00 0.104
## [279,] 10.3 0.320 0.45 6.40 0.073
## [280,] 8.9 0.400 0.32 5.60 0.087
## [281,] 11.4 0.260 0.44 3.60 0.071
## [282,] 7.7 0.270 0.68 3.50 0.358
## [283,] 7.6 0.520 0.12 3.00 0.067
## [284,] 8.9 0.400 0.32 5.60 0.087
## [285,] 9.9 0.590 0.07 3.40 0.102
## [286,] 9.9 0.590 0.07 3.40 0.102
## [287,] 12.0 0.450 0.55 2.00 0.073
## [288,] 7.5 0.400 0.12 3.00 0.092
## [289,] 8.7 0.520 0.09 2.50 0.091
## [290,] 11.6 0.420 0.53 3.30 0.105
## [291,] 8.7 0.520 0.09 2.50 0.091
## [292,] 11.0 0.200 0.48 2.00 0.343
## [293,] 10.4 0.550 0.23 2.70 0.091
## [294,] 6.9 0.360 0.25 2.40 0.098
## [295,] 13.3 0.340 0.52 3.20 0.094
## [296,] 10.8 0.500 0.46 2.50 0.073
## [297,] 10.6 0.830 0.37 2.60 0.086
## [298,] 7.1 0.630 0.06 2.00 0.083
## [299,] 7.2 0.650 0.02 2.30 0.094
## [300,] 6.9 0.670 0.06 2.10 0.080
## [301,] 7.5 0.530 0.06 2.60 0.086
## [302,] 11.1 0.180 0.48 1.50 0.068
## [303,] 8.3 0.705 0.12 2.60 0.092
## [304,] 7.4 0.670 0.12 1.60 0.186
## [305,] 8.4 0.650 0.60 2.10 0.112
## [306,] 10.3 0.530 0.48 2.50 0.063
## [307,] 7.6 0.620 0.32 2.20 0.082
## [308,] 10.3 0.410 0.42 2.40 0.213
## [309,] 10.3 0.430 0.44 2.40 0.214
## [310,] 7.4 0.290 0.38 1.70 0.062
## [311,] 10.3 0.530 0.48 2.50 0.063
## [312,] 7.9 0.530 0.24 2.00 0.072
## [313,] 9.0 0.460 0.31 2.80 0.093
## [314,] 8.6 0.470 0.30 3.00 0.076
## [315,] 7.4 0.360 0.29 2.60 0.087
## [316,] 7.1 0.350 0.29 2.50 0.096
## [317,] 9.6 0.560 0.23 3.40 0.102
## [318,] 9.6 0.770 0.12 2.90 0.082
## [319,] 9.8 0.660 0.39 3.20 0.083
## [320,] 9.6 0.770 0.12 2.90 0.082
## [321,] 9.8 0.660 0.39 3.20 0.083
## [322,] 9.3 0.610 0.26 3.40 0.090
## [323,] 7.8 0.620 0.05 2.30 0.079
## [324,] 10.3 0.590 0.42 2.80 0.090
## [325,] 10.0 0.490 0.20 11.00 0.071
## [326,] 10.0 0.490 0.20 11.00 0.071
## [327,] 11.6 0.530 0.66 3.65 0.121
## [328,] 10.3 0.440 0.50 4.50 0.107
## [329,] 13.4 0.270 0.62 2.60 0.082
## [330,] 10.7 0.460 0.39 2.00 0.061
## [331,] 10.2 0.360 0.64 2.90 0.122
## [332,] 10.2 0.360 0.64 2.90 0.122
## [333,] 8.0 0.580 0.28 3.20 0.066
## [334,] 8.4 0.560 0.08 2.10 0.105
## [335,] 7.9 0.650 0.01 2.50 0.078
## [336,] 11.9 0.695 0.53 3.40 0.128
## [337,] 8.9 0.430 0.45 1.90 0.052
## [338,] 7.8 0.430 0.32 2.80 0.080
## [339,] 12.4 0.490 0.58 3.00 0.103
## [340,] 12.5 0.280 0.54 2.30 0.082
## [341,] 12.2 0.340 0.50 2.40 0.066
## [342,] 10.6 0.420 0.48 2.70 0.065
## [343,] 10.9 0.390 0.47 1.80 0.118
## [344,] 10.9 0.390 0.47 1.80 0.118
## [345,] 11.9 0.570 0.50 2.60 0.082
## [346,] 7.0 0.685 0.00 1.90 0.067
## [347,] 6.6 0.815 0.02 2.70 0.072
## [348,] 13.8 0.490 0.67 3.00 0.093
## [349,] 9.6 0.560 0.31 2.80 0.089
## [350,] 9.1 0.785 0.00 2.60 0.093
## [351,] 10.7 0.670 0.22 2.70 0.107
## [352,] 9.1 0.795 0.00 2.60 0.096
## [353,] 7.7 0.665 0.00 2.40 0.090
## [354,] 13.5 0.530 0.79 4.80 0.120
## [355,] 6.1 0.210 0.40 1.40 0.066
## [356,] 6.7 0.750 0.01 2.40 0.078
## [357,] 11.5 0.410 0.52 3.00 0.080
## [358,] 10.5 0.420 0.66 2.95 0.116
## [359,] 11.9 0.430 0.66 3.10 0.109
## [360,] 12.6 0.380 0.66 2.60 0.088
## [361,] 8.2 0.700 0.23 2.00 0.099
## [362,] 8.6 0.450 0.31 2.60 0.086
## [363,] 11.9 0.580 0.66 2.50 0.072
## [364,] 12.5 0.460 0.63 2.00 0.071
## [365,] 12.8 0.615 0.66 5.80 0.083
## [366,] 10.0 0.420 0.50 3.40 0.107
## [367,] 12.8 0.615 0.66 5.80 0.083
## [368,] 10.4 0.575 0.61 2.60 0.076
## [369,] 10.3 0.340 0.52 2.80 0.159
## [370,] 9.4 0.270 0.53 2.40 0.074
## [371,] 6.9 0.765 0.02 2.30 0.063
## [372,] 7.9 0.240 0.40 1.60 0.056
## [373,] 9.1 0.280 0.48 1.80 0.067
## [374,] 7.4 0.550 0.22 2.20 0.106
## [375,] 14.0 0.410 0.63 3.80 0.089
## [376,] 11.5 0.540 0.71 4.40 0.124
## [377,] 11.5 0.450 0.50 3.00 0.078
## [378,] 9.4 0.270 0.53 2.40 0.074
## [379,] 11.4 0.625 0.66 6.20 0.088
## [380,] 8.3 0.420 0.38 2.50 0.094
## [381,] 8.3 0.260 0.42 2.00 0.080
## [382,] 13.7 0.415 0.68 2.90 0.085
## [383,] 8.3 0.260 0.42 2.00 0.080
## [384,] 8.3 0.260 0.42 2.00 0.080
## [385,] 7.7 0.510 0.28 2.10 0.087
## [386,] 7.4 0.630 0.07 2.40 0.090
## [387,] 7.8 0.540 0.26 2.00 0.088
## [388,] 8.3 0.660 0.15 1.90 0.079
## [389,] 7.8 0.460 0.26 1.90 0.088
## [390,] 9.6 0.380 0.31 2.50 0.096
## [391,] 5.6 0.850 0.05 1.40 0.045
## [392,] 13.7 0.415 0.68 2.90 0.085
## [393,] 9.5 0.370 0.52 2.00 0.082
## [394,] 8.4 0.665 0.61 2.00 0.112
## [395,] 12.7 0.600 0.65 2.30 0.063
## [396,] 12.0 0.370 0.76 4.20 0.066
## [397,] 6.6 0.735 0.02 7.90 0.122
## [398,] 11.5 0.590 0.59 2.60 0.087
## [399,] 11.5 0.590 0.59 2.60 0.087
## [400,] 8.7 0.765 0.22 2.30 0.064
## [401,] 6.6 0.735 0.02 7.90 0.122
## [402,] 7.7 0.260 0.30 1.70 0.059
## [403,] 12.2 0.480 0.54 2.60 0.085
## [404,] 11.4 0.600 0.49 2.70 0.085
## [405,] 7.7 0.690 0.05 2.70 0.075
## [406,] 8.7 0.310 0.46 1.40 0.059
## [407,] 9.8 0.440 0.47 2.50 0.063
## [408,] 12.0 0.390 0.66 3.00 0.093
## [409,] 10.4 0.340 0.58 3.70 0.174
## [410,] 12.5 0.460 0.49 4.50 0.070
## [411,] 9.0 0.430 0.34 2.50 0.080
## [412,] 9.1 0.450 0.35 2.40 0.080
## [413,] 7.1 0.735 0.16 1.90 0.100
## [414,] 9.9 0.400 0.53 6.70 0.097
## [415,] 8.8 0.520 0.34 2.70 0.087
## [416,] 8.6 0.725 0.24 6.60 0.117
## [417,] 10.6 0.480 0.64 2.20 0.111
## [418,] 7.0 0.580 0.12 1.90 0.091
## [419,] 11.9 0.380 0.51 2.00 0.121
## [420,] 6.8 0.770 0.00 1.80 0.066
## [421,] 9.5 0.560 0.33 2.40 0.089
## [422,] 6.6 0.840 0.03 2.30 0.059
## [423,] 7.7 0.960 0.20 2.00 0.047
## [424,] 10.5 0.240 0.47 2.10 0.066
## [425,] 7.7 0.960 0.20 2.00 0.047
## [426,] 6.6 0.840 0.03 2.30 0.059
## [427,] 6.4 0.670 0.08 2.10 0.045
## [428,] 9.5 0.780 0.22 1.90 0.077
## [429,] 9.1 0.520 0.33 1.30 0.070
## [430,] 12.8 0.840 0.63 2.40 0.088
## [431,] 10.5 0.240 0.47 2.10 0.066
## [432,] 7.8 0.550 0.35 2.20 0.074
## [433,] 11.9 0.370 0.69 2.30 0.078
## [434,] 12.3 0.390 0.63 2.30 0.091
## [435,] 10.4 0.410 0.55 3.20 0.076
## [436,] 12.3 0.390 0.63 2.30 0.091
## [437,] 8.0 0.670 0.30 2.00 0.060
## [438,] 11.1 0.450 0.73 3.20 0.066
## [439,] 10.4 0.410 0.55 3.20 0.076
## [440,] 7.0 0.620 0.18 1.50 0.062
## [441,] 12.6 0.310 0.72 2.20 0.072
## [442,] 11.9 0.400 0.65 2.15 0.068
## [443,] 15.6 0.685 0.76 3.70 0.100
## [444,] 10.0 0.440 0.49 2.70 0.077
## [445,] 5.3 0.570 0.01 1.70 0.054
## [446,] 9.5 0.735 0.10 2.10 0.079
## [447,] 12.5 0.380 0.60 2.60 0.081
## [448,] 9.3 0.480 0.29 2.10 0.127
## [449,] 8.6 0.530 0.22 2.00 0.100
## [450,] 11.9 0.390 0.69 2.80 0.095
## [451,] 11.9 0.390 0.69 2.80 0.095
## [452,] 8.4 0.370 0.53 1.80 0.413
## [453,] 6.8 0.560 0.03 1.70 0.084
## [454,] 10.4 0.330 0.63 2.80 0.084
## [455,] 7.0 0.230 0.40 1.60 0.063
## [456,] 11.3 0.620 0.67 5.20 0.086
## [457,] 8.9 0.590 0.39 2.30 0.095
## [458,] 9.2 0.630 0.21 2.70 0.097
## [459,] 10.4 0.330 0.63 2.80 0.084
## [460,] 11.6 0.580 0.66 2.20 0.074
## [461,] 9.2 0.430 0.52 2.30 0.083
## [462,] 8.3 0.615 0.22 2.60 0.087
## [463,] 11.0 0.260 0.68 2.55 0.085
## [464,] 8.1 0.660 0.70 2.20 0.098
## [465,] 11.5 0.315 0.54 2.10 0.084
## [466,] 10.0 0.290 0.40 2.90 0.098
## [467,] 10.3 0.500 0.42 2.00 0.069
## [468,] 8.8 0.460 0.45 2.60 0.065
## [469,] 11.4 0.360 0.69 2.10 0.090
## [470,] 8.7 0.820 0.02 1.20 0.070
## [471,] 13.0 0.320 0.65 2.60 0.093
## [472,] 9.6 0.540 0.42 2.40 0.081
## [473,] 12.5 0.370 0.55 2.60 0.083
## [474,] 9.9 0.350 0.55 2.10 0.062
## [475,] 10.5 0.280 0.51 1.70 0.080
## [476,] 9.6 0.680 0.24 2.20 0.087
## [477,] 9.3 0.270 0.41 2.00 0.091
## [478,] 10.4 0.240 0.49 1.80 0.075
## [479,] 9.6 0.680 0.24 2.20 0.087
## [480,] 9.4 0.685 0.11 2.70 0.077
## [481,] 10.6 0.280 0.39 15.50 0.069
## [482,] 9.4 0.300 0.56 2.80 0.080
## [483,] 10.6 0.360 0.59 2.20 0.152
## [484,] 10.6 0.360 0.60 2.20 0.152
## [485,] 10.6 0.440 0.68 4.10 0.114
## [486,] 10.2 0.670 0.39 1.90 0.054
## [487,] 10.2 0.670 0.39 1.90 0.054
## [488,] 10.2 0.645 0.36 1.80 0.053
## [489,] 11.6 0.320 0.55 2.80 0.081
## [490,] 9.3 0.390 0.40 2.60 0.073
## [491,] 9.3 0.775 0.27 2.80 0.078
## [492,] 9.2 0.410 0.50 2.50 0.055
## [493,] 8.9 0.400 0.51 2.60 0.052
## [494,] 8.7 0.690 0.31 3.00 0.086
## [495,] 6.5 0.390 0.23 8.30 0.051
## [496,] 10.7 0.350 0.53 2.60 0.070
## [497,] 7.8 0.520 0.25 1.90 0.081
## [498,] 7.2 0.340 0.32 2.50 0.090
## [499,] 10.7 0.350 0.53 2.60 0.070
## [500,] 8.7 0.690 0.31 3.00 0.086
## [501,] 7.8 0.520 0.25 1.90 0.081
## [502,] 10.4 0.440 0.73 6.55 0.074
## [503,] 10.4 0.440 0.73 6.55 0.074
## [504,] 10.5 0.260 0.47 1.90 0.078
## [505,] 10.5 0.240 0.42 1.80 0.077
## [506,] 10.2 0.490 0.63 2.90 0.072
## [507,] 10.4 0.240 0.46 1.80 0.075
## [508,] 11.2 0.670 0.55 2.30 0.084
## [509,] 10.0 0.590 0.31 2.20 0.090
## [510,] 13.3 0.290 0.75 2.80 0.084
## [511,] 12.4 0.420 0.49 4.60 0.073
## [512,] 10.0 0.590 0.31 2.20 0.090
## [513,] 10.7 0.400 0.48 2.10 0.125
## [514,] 10.5 0.510 0.64 2.40 0.107
## [515,] 10.5 0.510 0.64 2.40 0.107
## [516,] 8.5 0.655 0.49 6.10 0.122
## [517,] 12.5 0.600 0.49 4.30 0.100
## [518,] 10.4 0.610 0.49 2.10 0.200
## [519,] 10.9 0.210 0.49 2.80 0.088
## [520,] 7.3 0.365 0.49 2.50 0.088
## [521,] 9.8 0.250 0.49 2.70 0.088
## [522,] 7.6 0.410 0.49 2.00 0.088
## [523,] 8.2 0.390 0.49 2.30 0.099
## [524,] 9.3 0.400 0.49 2.50 0.085
## [525,] 9.2 0.430 0.49 2.40 0.086
## [526,] 10.4 0.640 0.24 2.80 0.105
## [527,] 7.3 0.365 0.49 2.50 0.088
## [528,] 7.0 0.380 0.49 2.50 0.097
## [529,] 8.2 0.420 0.49 2.60 0.084
## [530,] 9.9 0.630 0.24 2.40 0.077
## [531,] 9.1 0.220 0.24 2.10 0.078
## [532,] 11.9 0.380 0.49 2.70 0.098
## [533,] 11.9 0.380 0.49 2.70 0.098
## [534,] 10.3 0.270 0.24 2.10 0.072
## [535,] 10.0 0.480 0.24 2.70 0.102
## [536,] 9.1 0.220 0.24 2.10 0.078
## [537,] 9.9 0.630 0.24 2.40 0.077
## [538,] 8.1 0.825 0.24 2.10 0.084
## [539,] 12.9 0.350 0.49 5.80 0.066
## [540,] 11.2 0.500 0.74 5.15 0.100
## [541,] 9.2 0.590 0.24 3.30 0.101
## [542,] 9.5 0.460 0.49 6.30 0.064
## [543,] 9.3 0.715 0.24 2.10 0.070
## [544,] 11.2 0.660 0.24 2.50 0.085
## [545,] 14.3 0.310 0.74 1.80 0.075
## [546,] 9.1 0.470 0.49 2.60 0.094
## [547,] 7.5 0.550 0.24 2.00 0.078
## [548,] 10.6 0.310 0.49 2.50 0.067
## [549,] 12.4 0.350 0.49 2.60 0.079
## [550,] 9.0 0.530 0.49 1.90 0.171
## [551,] 6.8 0.510 0.01 2.10 0.074
## [552,] 9.4 0.430 0.24 2.80 0.092
## [553,] 9.5 0.460 0.24 2.70 0.092
## [554,] 5.0 1.040 0.24 1.60 0.050
## [555,] 15.5 0.645 0.49 4.20 0.095
## [556,] 15.5 0.645 0.49 4.20 0.095
## [557,] 10.9 0.530 0.49 4.60 0.118
## [558,] 15.6 0.645 0.49 4.20 0.095
## [559,] 10.9 0.530 0.49 4.60 0.118
## [560,] 13.0 0.470 0.49 4.30 0.085
## [561,] 12.7 0.600 0.49 2.80 0.075
## [562,] 9.0 0.440 0.49 2.40 0.078
## [563,] 9.0 0.540 0.49 2.90 0.094
## [564,] 7.6 0.290 0.49 2.70 0.092
## [565,] 13.0 0.470 0.49 4.30 0.085
## [566,] 12.7 0.600 0.49 2.80 0.075
## [567,] 8.7 0.700 0.24 2.50 0.226
## [568,] 8.7 0.700 0.24 2.50 0.226
## [569,] 9.8 0.500 0.49 2.60 0.250
## [570,] 6.2 0.360 0.24 2.20 0.095
## [571,] 11.5 0.350 0.49 3.30 0.070
## [572,] 6.2 0.360 0.24 2.20 0.095
## [573,] 10.2 0.240 0.49 2.40 0.075
## [574,] 10.5 0.590 0.49 2.10 0.070
## [575,] 10.6 0.340 0.49 3.20 0.078
## [576,] 12.3 0.270 0.49 3.10 0.079
## [577,] 9.9 0.500 0.24 2.30 0.103
## [578,] 8.8 0.440 0.49 2.80 0.083
## [579,] 8.8 0.470 0.49 2.90 0.085
## [580,] 10.6 0.310 0.49 2.20 0.063
## [581,] 12.3 0.500 0.49 2.20 0.089
## [582,] 12.3 0.500 0.49 2.20 0.089
## [583,] 11.7 0.490 0.49 2.20 0.083
## [584,] 12.0 0.280 0.49 1.90 0.074
## [585,] 11.8 0.330 0.49 3.40 0.093
## [586,] 7.6 0.510 0.24 2.40 0.091
## [587,] 11.1 0.310 0.49 2.70 0.094
## [588,] 7.3 0.730 0.24 1.90 0.108
## [589,] 5.0 0.420 0.24 2.00 0.060
## [590,] 10.2 0.290 0.49 2.60 0.059
## [591,] 9.0 0.450 0.49 2.60 0.084
## [592,] 6.6 0.390 0.49 1.70 0.070
## [593,] 9.0 0.450 0.49 2.60 0.084
## [594,] 9.9 0.490 0.58 3.50 0.094
## [595,] 7.9 0.720 0.17 2.60 0.096
## [596,] 8.9 0.595 0.41 7.90 0.086
## [597,] 12.4 0.400 0.51 2.00 0.059
## [598,] 11.9 0.580 0.58 1.90 0.071
## [599,] 8.5 0.585 0.18 2.10 0.078
## [600,] 12.7 0.590 0.45 2.30 0.082
## [601,] 8.2 0.915 0.27 2.10 0.088
## [602,] 13.2 0.460 0.52 2.20 0.071
## [603,] 7.7 0.835 0.00 2.60 0.081
## [604,] 13.2 0.460 0.52 2.20 0.071
## [605,] 8.3 0.580 0.13 2.90 0.096
## [606,] 8.3 0.600 0.13 2.60 0.085
## [607,] 9.4 0.410 0.48 4.60 0.072
## [608,] 8.8 0.480 0.41 3.30 0.092
## [609,] 10.1 0.650 0.37 5.10 0.110
## [610,] 6.3 0.360 0.19 3.20 0.075
## [611,] 8.8 0.240 0.54 2.50 0.083
## [612,] 13.2 0.380 0.55 2.70 0.081
## [613,] 7.5 0.640 0.00 2.40 0.077
## [614,] 8.2 0.390 0.38 1.50 0.058
## [615,] 9.2 0.755 0.18 2.20 0.148
## [616,] 9.6 0.600 0.50 2.30 0.079
## [617,] 9.6 0.600 0.50 2.30 0.079
## [618,] 11.5 0.310 0.51 2.20 0.079
## [619,] 11.4 0.460 0.50 2.70 0.122
## [620,] 11.3 0.370 0.41 2.30 0.088
## [621,] 8.3 0.540 0.24 3.40 0.076
## [622,] 8.2 0.560 0.23 3.40 0.078
## [623,] 10.0 0.580 0.22 1.90 0.080
## [624,] 7.9 0.510 0.25 2.90 0.077
## [625,] 6.8 0.690 0.00 5.60 0.124
## [626,] 6.8 0.690 0.00 5.60 0.124
## [627,] 8.8 0.600 0.29 2.20 0.098
## [628,] 8.8 0.600 0.29 2.20 0.098
## [629,] 8.7 0.540 0.26 2.50 0.097
## [630,] 7.6 0.685 0.23 2.30 0.111
## [631,] 8.7 0.540 0.26 2.50 0.097
## [632,] 10.4 0.280 0.54 2.70 0.105
## [633,] 7.6 0.410 0.14 3.00 0.087
## [634,] 10.1 0.935 0.22 3.40 0.105
## [635,] 7.9 0.350 0.21 1.90 0.073
## [636,] 8.7 0.840 0.00 1.40 0.065
## [637,] 9.6 0.880 0.28 2.40 0.086
## [638,] 9.5 0.885 0.27 2.30 0.084
## [639,] 7.7 0.915 0.12 2.20 0.143
## [640,] 8.9 0.290 0.35 1.90 0.067
## [641,] 9.9 0.540 0.45 2.30 0.071
## [642,] 9.5 0.590 0.44 2.30 0.071
## [643,] 9.9 0.540 0.45 2.30 0.071
## [644,] 9.5 0.590 0.44 2.30 0.071
## [645,] 9.9 0.540 0.45 2.30 0.071
## [646,] 7.8 0.640 0.10 6.00 0.115
## [647,] 7.3 0.670 0.05 3.60 0.107
## [648,] 8.3 0.845 0.01 2.20 0.070
## [649,] 8.7 0.480 0.30 2.80 0.066
## [650,] 6.7 0.420 0.27 8.60 0.068
## [651,] 10.7 0.430 0.39 2.20 0.106
## [652,] 9.8 0.880 0.25 2.50 0.104
## [653,] 15.9 0.360 0.65 7.50 0.096
## [654,] 9.4 0.330 0.59 2.80 0.079
## [655,] 8.6 0.470 0.47 2.40 0.074
## [656,] 9.7 0.550 0.17 2.90 0.087
## [657,] 10.7 0.430 0.39 2.20 0.106
## [658,] 12.0 0.500 0.59 1.40 0.073
## [659,] 7.2 0.520 0.07 1.40 0.074
## [660,] 7.1 0.840 0.02 4.40 0.096
## [661,] 7.2 0.520 0.07 1.40 0.074
## [662,] 7.5 0.420 0.31 1.60 0.080
## [663,] 7.2 0.570 0.06 1.60 0.076
## [664,] 10.1 0.280 0.46 1.80 0.050
## [665,] 12.1 0.400 0.52 2.00 0.092
## [666,] 9.4 0.590 0.14 2.00 0.084
## [667,] 8.3 0.490 0.36 1.80 0.222
## [668,] 11.3 0.340 0.45 2.00 0.082
## [669,] 10.0 0.730 0.43 2.30 0.059
## [670,] 11.3 0.340 0.45 2.00 0.082
## [671,] 6.9 0.400 0.24 2.50 0.083
## [672,] 8.2 0.730 0.21 1.70 0.074
## [673,] 9.8 1.240 0.34 2.00 0.079
## [674,] 8.2 0.730 0.21 1.70 0.074
## [675,] 10.8 0.400 0.41 2.20 0.084
## [676,] 9.3 0.410 0.39 2.20 0.064
## [677,] 10.8 0.400 0.41 2.20 0.084
## [678,] 8.6 0.800 0.11 2.30 0.084
## [679,] 8.3 0.780 0.10 2.60 0.081
## [680,] 10.8 0.260 0.45 3.30 0.060
## [681,] 13.3 0.430 0.58 1.90 0.070
## [682,] 8.0 0.450 0.23 2.20 0.094
## [683,] 8.5 0.460 0.31 2.25 0.078
## [684,] 8.1 0.780 0.23 2.60 0.059
## [685,] 9.8 0.980 0.32 2.30 0.078
## [686,] 8.1 0.780 0.23 2.60 0.059
## [687,] 7.1 0.650 0.18 1.80 0.070
## [688,] 9.1 0.640 0.23 3.10 0.095
## [689,] 7.7 0.660 0.04 1.60 0.039
## [690,] 8.1 0.380 0.48 1.80 0.157
## [691,] 7.4 1.185 0.00 4.25 0.097
## [692,] 9.2 0.920 0.24 2.60 0.087
## [693,] 8.6 0.490 0.51 2.00 0.422
## [694,] 9.0 0.480 0.32 2.80 0.084
## [695,] 9.0 0.470 0.31 2.70 0.084
## [696,] 5.1 0.470 0.02 1.30 0.034
## [697,] 7.0 0.650 0.02 2.10 0.066
## [698,] 7.0 0.650 0.02 2.10 0.066
## [699,] 9.4 0.615 0.28 3.20 0.087
## [700,] 11.8 0.380 0.55 2.10 0.071
## [701,] 10.6 1.020 0.43 2.90 0.076
## [702,] 7.0 0.650 0.02 2.10 0.066
## [703,] 7.0 0.640 0.02 2.10 0.067
## [704,] 7.5 0.380 0.48 2.60 0.073
## [705,] 9.1 0.765 0.04 1.60 0.078
## [706,] 8.4 1.035 0.15 6.00 0.073
## [707,] 7.0 0.780 0.08 2.00 0.093
## [708,] 7.4 0.490 0.19 3.00 0.077
## [709,] 7.8 0.545 0.12 2.50 0.068
## [710,] 9.7 0.310 0.47 1.60 0.062
## [711,] 10.6 1.025 0.43 2.80 0.080
## [712,] 8.9 0.565 0.34 3.00 0.093
## [713,] 8.7 0.690 0.00 3.20 0.084
## [714,] 8.0 0.430 0.36 2.30 0.075
## [715,] 9.9 0.740 0.28 2.60 0.078
## [716,] 7.2 0.490 0.18 2.70 0.069
## [717,] 8.0 0.430 0.36 2.30 0.075
## [718,] 7.6 0.460 0.11 2.60 0.079
## [719,] 8.4 0.560 0.04 2.00 0.082
## [720,] 7.1 0.660 0.00 3.90 0.086
## [721,] 8.4 0.560 0.04 2.00 0.082
## [722,] 8.9 0.480 0.24 2.85 0.094
## [723,] 7.6 0.420 0.08 2.70 0.084
## [724,] 7.1 0.310 0.30 2.20 0.053
## [725,] 7.5 1.115 0.10 3.10 0.086
## [726,] 9.0 0.660 0.17 3.00 0.077
## [727,] 8.1 0.720 0.09 2.80 0.084
## [728,] 6.4 0.570 0.02 1.80 0.067
## [729,] 6.4 0.570 0.02 1.80 0.067
## [730,] 6.4 0.865 0.03 3.20 0.071
## [731,] 9.5 0.550 0.66 2.30 0.387
## [732,] 8.9 0.875 0.13 3.45 0.088
## [733,] 7.3 0.835 0.03 2.10 0.092
## [734,] 7.0 0.450 0.34 2.70 0.082
## [735,] 7.7 0.560 0.20 2.00 0.075
## [736,] 7.7 0.965 0.10 2.10 0.112
## [737,] 7.7 0.965 0.10 2.10 0.112
## [738,] 8.2 0.590 0.00 2.50 0.093
## [739,] 9.0 0.460 0.23 2.80 0.092
## [740,] 9.0 0.690 0.00 2.40 0.088
## [741,] 8.3 0.760 0.29 4.20 0.075
## [742,] 9.2 0.530 0.24 2.60 0.078
## [743,] 6.5 0.615 0.00 1.90 0.065
## [744,] 11.6 0.410 0.58 2.80 0.096
## [745,] 11.1 0.390 0.54 2.70 0.095
## [746,] 7.3 0.510 0.18 2.10 0.070
## [747,] 8.2 0.340 0.38 2.50 0.080
## [748,] 8.6 0.330 0.40 2.60 0.083
## [749,] 7.2 0.500 0.18 2.10 0.071
## [750,] 7.3 0.510 0.18 2.10 0.070
## [751,] 8.3 0.650 0.10 2.90 0.089
## [752,] 8.3 0.650 0.10 2.90 0.089
## [753,] 7.6 0.540 0.13 2.50 0.097
## [754,] 8.3 0.650 0.10 2.90 0.089
## [755,] 7.8 0.480 0.68 1.70 0.415
## [756,] 7.8 0.910 0.07 1.90 0.058
## [757,] 6.3 0.980 0.01 2.00 0.057
## [758,] 8.1 0.870 0.00 2.20 0.084
## [759,] 8.1 0.870 0.00 2.20 0.084
## [760,] 8.8 0.420 0.21 2.50 0.092
## [761,] 9.0 0.580 0.25 2.80 0.075
## [762,] 9.3 0.655 0.26 2.00 0.096
## [763,] 8.8 0.700 0.00 1.70 0.069
## [764,] 9.3 0.655 0.26 2.00 0.096
## [765,] 9.1 0.680 0.11 2.80 0.093
## [766,] 9.2 0.670 0.10 3.00 0.091
## [767,] 8.8 0.590 0.18 2.90 0.089
## [768,] 7.5 0.600 0.32 2.70 0.103
## [769,] 7.1 0.590 0.02 2.30 0.082
## [770,] 7.9 0.720 0.01 1.90 0.076
## [771,] 7.1 0.590 0.02 2.30 0.082
## [772,] 9.4 0.685 0.26 2.40 0.082
## [773,] 9.5 0.570 0.27 2.30 0.082
## [774,] 7.9 0.400 0.29 1.80 0.157
## [775,] 7.9 0.400 0.30 1.80 0.157
## [776,] 7.2 1.000 0.00 3.00 0.102
## [777,] 6.9 0.765 0.18 2.40 0.243
## [778,] 6.9 0.635 0.17 2.40 0.241
## [779,] 8.3 0.430 0.30 3.40 0.079
## [780,] 7.1 0.520 0.03 2.60 0.076
## [781,] 7.0 0.570 0.00 2.00 0.190
## [782,] 6.5 0.460 0.14 2.40 0.114
## [783,] 9.0 0.820 0.05 2.40 0.081
## [784,] 6.5 0.460 0.14 2.40 0.114
## [785,] 7.1 0.590 0.01 2.50 0.077
## [786,] 9.9 0.350 0.41 2.30 0.083
## [787,] 9.9 0.350 0.41 2.30 0.083
## [788,] 10.0 0.560 0.24 2.20 0.079
## [789,] 10.0 0.560 0.24 2.20 0.079
## [790,] 8.6 0.630 0.17 2.90 0.099
## [791,] 7.4 0.370 0.43 2.60 0.082
## [792,] 8.8 0.640 0.17 2.90 0.084
## [793,] 7.1 0.610 0.02 2.50 0.081
## [794,] 7.7 0.600 0.00 2.60 0.055
## [795,] 10.1 0.270 0.54 2.30 0.065
## [796,] 10.8 0.890 0.30 2.60 0.132
## [797,] 8.7 0.460 0.31 2.50 0.126
## [798,] 9.3 0.370 0.44 1.60 0.038
## [799,] 9.4 0.500 0.34 3.60 0.082
## [800,] 9.4 0.500 0.34 3.60 0.082
## [801,] 7.2 0.610 0.08 4.00 0.082
## [802,] 8.6 0.550 0.09 3.30 0.068
## [803,] 5.1 0.585 0.00 1.70 0.044
## [804,] 7.7 0.560 0.08 2.50 0.114
## [805,] 8.4 0.520 0.22 2.70 0.084
## [806,] 8.2 0.280 0.40 2.40 0.052
## [807,] 8.4 0.250 0.39 2.00 0.041
## [808,] 8.2 0.280 0.40 2.40 0.052
## [809,] 7.4 0.530 0.12 1.90 0.165
## [810,] 7.6 0.480 0.31 2.80 0.070
## [811,] 7.3 0.490 0.10 2.60 0.068
## [812,] 12.9 0.500 0.55 2.80 0.072
## [813,] 10.8 0.450 0.33 2.50 0.099
## [814,] 6.9 0.390 0.24 2.10 0.102
## [815,] 12.6 0.410 0.54 2.80 0.103
## [816,] 10.8 0.450 0.33 2.50 0.099
## [817,] 9.8 0.510 0.19 3.20 0.081
## [818,] 10.8 0.290 0.42 1.60 0.084
## [819,] 7.1 0.715 0.00 2.35 0.071
## [820,] 9.1 0.660 0.15 3.20 0.097
## [821,] 7.0 0.685 0.00 1.90 0.099
## [822,] 4.9 0.420 0.00 2.10 0.048
## [823,] 6.7 0.540 0.13 2.00 0.076
## [824,] 6.7 0.540 0.13 2.00 0.076
## [825,] 7.1 0.480 0.28 2.80 0.068
## [826,] 7.1 0.460 0.14 2.80 0.076
## [827,] 7.5 0.270 0.34 2.30 0.050
## [828,] 7.1 0.460 0.14 2.80 0.076
## [829,] 7.8 0.570 0.09 2.30 0.065
## [830,] 5.9 0.610 0.08 2.10 0.071
## [831,] 7.5 0.685 0.07 2.50 0.058
## [832,] 5.9 0.610 0.08 2.10 0.071
## [833,] 10.4 0.440 0.42 1.50 0.145
## [834,] 11.6 0.470 0.44 1.60 0.147
## [835,] 8.8 0.685 0.26 1.60 0.088
## [836,] 7.6 0.665 0.10 1.50 0.066
## [837,] 6.7 0.280 0.28 2.40 0.012
## [838,] 6.7 0.280 0.28 2.40 0.012
## [839,] 10.1 0.310 0.35 1.60 0.075
## [840,] 6.0 0.500 0.04 2.20 0.092
## [841,] 11.1 0.420 0.47 2.65 0.085
## [842,] 6.6 0.660 0.00 3.00 0.115
## [843,] 10.6 0.500 0.45 2.60 0.119
## [844,] 7.1 0.685 0.35 2.00 0.088
## [845,] 9.9 0.250 0.46 1.70 0.062
## [846,] 6.4 0.640 0.21 1.80 0.081
## [847,] 6.4 0.640 0.21 1.80 0.081
## [848,] 7.4 0.680 0.16 1.80 0.078
## [849,] 6.4 0.640 0.21 1.80 0.081
## [850,] 6.4 0.630 0.21 1.60 0.080
## [851,] 9.3 0.430 0.44 1.90 0.085
## [852,] 9.3 0.430 0.44 1.90 0.085
## [853,] 8.0 0.420 0.32 2.50 0.080
## [854,] 9.3 0.360 0.39 1.50 0.080
## [855,] 9.3 0.360 0.39 1.50 0.080
## [856,] 7.6 0.735 0.02 2.50 0.071
## [857,] 9.3 0.360 0.39 1.50 0.080
## [858,] 8.2 0.260 0.34 2.50 0.073
## [859,] 11.7 0.280 0.47 1.70 0.054
## [860,] 6.8 0.560 0.22 1.80 0.074
## [861,] 7.2 0.620 0.06 2.70 0.077
## [862,] 5.8 1.010 0.66 2.00 0.039
## [863,] 7.5 0.420 0.32 2.70 0.067
## [864,] 7.2 0.620 0.06 2.50 0.078
## [865,] 7.2 0.620 0.06 2.70 0.077
## [866,] 7.2 0.635 0.07 2.60 0.077
## [867,] 6.8 0.490 0.22 2.30 0.071
## [868,] 6.9 0.510 0.23 2.00 0.072
## [869,] 6.8 0.560 0.22 1.80 0.074
## [870,] 7.6 0.630 0.03 2.00 0.080
## [871,] 7.7 0.715 0.01 2.10 0.064
## [872,] 6.9 0.560 0.03 1.50 0.086
## [873,] 7.3 0.350 0.24 2.00 0.067
## [874,] 9.1 0.210 0.37 1.60 0.067
## [875,] 10.4 0.380 0.46 2.10 0.104
## [876,] 8.8 0.310 0.40 2.80 0.109
## [877,] 7.1 0.470 0.00 2.20 0.067
## [878,] 7.7 0.715 0.01 2.10 0.064
## [879,] 8.8 0.610 0.19 4.00 0.094
## [880,] 7.2 0.600 0.04 2.50 0.076
## [881,] 9.2 0.560 0.18 1.60 0.078
## [882,] 7.6 0.715 0.00 2.10 0.068
## [883,] 8.4 0.310 0.29 3.10 0.194
## [884,] 7.2 0.600 0.04 2.50 0.076
## [885,] 8.8 0.610 0.19 4.00 0.094
## [886,] 8.9 0.750 0.14 2.50 0.086
## [887,] 9.0 0.800 0.12 2.40 0.083
## [888,] 10.7 0.520 0.38 2.60 0.066
## [889,] 6.8 0.570 0.00 2.50 0.072
## [890,] 10.7 0.900 0.34 6.60 0.112
## [891,] 7.2 0.340 0.24 2.00 0.071
## [892,] 7.2 0.660 0.03 2.30 0.078
## [893,] 10.1 0.450 0.23 1.90 0.082
## [894,] 7.2 0.660 0.03 2.30 0.078
## [895,] 7.2 0.630 0.03 2.20 0.080
## [896,] 7.1 0.590 0.01 2.30 0.080
## [897,] 8.3 0.310 0.39 2.40 0.078
## [898,] 7.1 0.590 0.01 2.30 0.080
## [899,] 8.3 0.310 0.39 2.40 0.078
## [900,] 8.3 1.020 0.02 3.40 0.084
## [901,] 8.9 0.310 0.36 2.60 0.056
## [902,] 7.4 0.635 0.10 2.40 0.080
## [903,] 7.4 0.635 0.10 2.40 0.080
## [904,] 6.8 0.590 0.06 6.00 0.060
## [905,] 6.8 0.590 0.06 6.00 0.060
## [906,] 9.2 0.580 0.20 3.00 0.081
## [907,] 7.2 0.540 0.27 2.60 0.084
## [908,] 6.1 0.560 0.00 2.20 0.079
## [909,] 7.4 0.520 0.13 2.40 0.078
## [910,] 7.3 0.305 0.39 1.20 0.059
## [911,] 9.3 0.380 0.48 3.80 0.132
## [912,] 9.1 0.280 0.46 9.00 0.114
## [913,] 10.0 0.460 0.44 2.90 0.065
## [914,] 9.4 0.395 0.46 4.60 0.094
## [915,] 7.3 0.305 0.39 1.20 0.059
## [916,] 8.6 0.315 0.40 2.20 0.079
## [917,] 5.3 0.715 0.19 1.50 0.161
## [918,] 6.8 0.410 0.31 8.80 0.084
## [919,] 8.4 0.360 0.32 2.20 0.081
## [920,] 8.4 0.620 0.12 1.80 0.072
## [921,] 9.6 0.410 0.37 2.30 0.091
## [922,] 8.4 0.360 0.32 2.20 0.081
## [923,] 8.4 0.620 0.12 1.80 0.072
## [924,] 6.8 0.410 0.31 8.80 0.084
## [925,] 8.6 0.470 0.27 2.30 0.055
## [926,] 8.6 0.220 0.36 1.90 0.064
## [927,] 9.4 0.240 0.33 2.30 0.061
## [928,] 8.4 0.670 0.19 2.20 0.093
## [929,] 8.6 0.470 0.27 2.30 0.055
## [930,] 8.7 0.330 0.38 3.30 0.063
## [931,] 6.6 0.610 0.01 1.90 0.080
## [932,] 7.4 0.610 0.01 2.00 0.074
## [933,] 7.6 0.400 0.29 1.90 0.078
## [934,] 7.4 0.610 0.01 2.00 0.074
## [935,] 6.6 0.610 0.01 1.90 0.080
## [936,] 8.8 0.300 0.38 2.30 0.060
## [937,] 8.8 0.300 0.38 2.30 0.060
## [938,] 12.0 0.630 0.50 1.40 0.071
## [939,] 7.2 0.380 0.38 2.80 0.068
## [940,] 6.2 0.460 0.17 1.60 0.073
## [941,] 9.6 0.330 0.52 2.20 0.074
## [942,] 9.9 0.270 0.49 5.00 0.082
## [943,] 10.1 0.430 0.40 2.60 0.092
## [944,] 9.8 0.500 0.34 2.30 0.094
## [945,] 8.3 0.300 0.49 3.80 0.090
## [946,] 10.2 0.440 0.42 2.00 0.071
## [947,] 10.2 0.440 0.58 4.10 0.092
## [948,] 8.3 0.280 0.48 2.10 0.093
## [949,] 8.9 0.120 0.45 1.80 0.075
## [950,] 8.9 0.120 0.45 1.80 0.075
## [951,] 8.9 0.120 0.45 1.80 0.075
## [952,] 8.3 0.280 0.48 2.10 0.093
## [953,] 8.2 0.310 0.40 2.20 0.058
## [954,] 10.2 0.340 0.48 2.10 0.052
## [955,] 7.6 0.430 0.40 2.70 0.082
## [956,] 8.5 0.210 0.52 1.90 0.090
## [957,] 9.0 0.360 0.52 2.10 0.111
## [958,] 9.5 0.370 0.52 2.00 0.088
## [959,] 6.4 0.570 0.12 2.30 0.120
## [960,] 8.0 0.590 0.05 2.00 0.089
## [961,] 8.5 0.470 0.27 1.90 0.058
## [962,] 7.1 0.560 0.14 1.60 0.078
## [963,] 6.6 0.570 0.02 2.10 0.115
## [964,] 8.8 0.270 0.39 2.00 0.100
## [965,] 8.5 0.470 0.27 1.90 0.058
## [966,] 8.3 0.340 0.40 2.40 0.065
## [967,] 9.0 0.380 0.41 2.40 0.103
## [968,] 8.5 0.660 0.20 2.10 0.097
## [969,] 9.0 0.400 0.43 2.40 0.068
## [970,] 6.7 0.560 0.09 2.90 0.079
## [971,] 10.4 0.260 0.48 1.90 0.066
## [972,] 10.4 0.260 0.48 1.90 0.066
## [973,] 10.1 0.380 0.50 2.40 0.104
## [974,] 8.5 0.340 0.44 1.70 0.079
## [975,] 8.8 0.330 0.41 5.90 0.073
## [976,] 7.2 0.410 0.30 2.10 0.083
## [977,] 7.2 0.410 0.30 2.10 0.083
## [978,] 8.4 0.590 0.29 2.60 0.109
## [979,] 7.0 0.400 0.32 3.60 0.061
## [980,] 12.2 0.450 0.49 1.40 0.075
## [981,] 9.1 0.500 0.30 1.90 0.065
## [982,] 9.5 0.860 0.26 1.90 0.079
## [983,] 7.3 0.520 0.32 2.10 0.070
## [984,] 9.1 0.500 0.30 1.90 0.065
## [985,] 12.2 0.450 0.49 1.40 0.075
## [986,] 7.4 0.580 0.00 2.00 0.064
## [987,] 9.8 0.340 0.39 1.40 0.066
## [988,] 7.1 0.360 0.30 1.60 0.080
## [989,] 7.7 0.390 0.12 1.70 0.097
## [990,] 9.7 0.295 0.40 1.50 0.073
## [991,] 7.7 0.390 0.12 1.70 0.097
## [992,] 7.1 0.340 0.28 2.00 0.082
## [993,] 6.5 0.400 0.10 2.00 0.076
## [994,] 7.1 0.340 0.28 2.00 0.082
## [995,] 10.0 0.350 0.45 2.50 0.092
## [996,] 7.7 0.600 0.06 2.00 0.079
## [997,] 5.6 0.660 0.00 2.20 0.087
## [998,] 5.6 0.660 0.00 2.20 0.087
## [999,] 8.9 0.840 0.34 1.40 0.050
## [1000,] 6.4 0.690 0.00 1.65 0.055
## [1001,] 7.5 0.430 0.30 2.20 0.062
## [1002,] 9.9 0.350 0.38 1.50 0.058
## [1003,] 9.1 0.290 0.33 2.05 0.063
## [1004,] 6.8 0.360 0.32 1.80 0.067
## [1005,] 8.2 0.430 0.29 1.60 0.081
## [1006,] 6.8 0.360 0.32 1.80 0.067
## [1007,] 9.1 0.290 0.33 2.05 0.063
## [1008,] 9.1 0.300 0.34 2.00 0.064
## [1009,] 8.9 0.350 0.40 3.60 0.110
## [1010,] 9.6 0.500 0.36 2.80 0.116
## [1011,] 8.9 0.280 0.45 1.70 0.067
## [1012,] 8.9 0.320 0.31 2.00 0.088
## [1013,] 7.7 1.005 0.15 2.10 0.102
## [1014,] 7.5 0.710 0.00 1.60 0.092
## [1015,] 8.0 0.580 0.16 2.00 0.120
## [1016,] 10.5 0.390 0.46 2.20 0.075
## [1017,] 8.9 0.380 0.40 2.20 0.068
## [1018,] 8.0 0.180 0.37 0.90 0.049
## [1019,] 8.0 0.180 0.37 0.90 0.049
## [1020,] 7.0 0.500 0.14 1.80 0.078
## [1021,] 11.3 0.360 0.66 2.40 0.123
## [1022,] 11.3 0.360 0.66 2.40 0.123
## [1023,] 7.0 0.510 0.09 2.10 0.062
## [1024,] 8.2 0.320 0.42 2.30 0.098
## [1025,] 7.7 0.580 0.01 1.80 0.088
## [1026,] 8.6 0.830 0.00 2.80 0.095
## [1027,] 7.9 0.310 0.32 1.90 0.066
## [1028,] 6.4 0.795 0.00 2.20 0.065
## [1029,] 7.2 0.340 0.21 2.50 0.075
## [1030,] 7.7 0.580 0.01 1.80 0.088
## [1031,] 7.1 0.590 0.00 2.10 0.091
## [1032,] 7.3 0.550 0.01 1.80 0.093
## [1033,] 8.1 0.820 0.00 4.10 0.095
## [1034,] 7.5 0.570 0.08 2.60 0.089
## [1035,] 8.9 0.745 0.18 2.50 0.077
## [1036,] 10.1 0.370 0.34 2.40 0.085
## [1037,] 7.6 0.310 0.34 2.50 0.082
## [1038,] 7.3 0.910 0.10 1.80 0.074
## [1039,] 8.7 0.410 0.41 6.20 0.078
## [1040,] 8.9 0.500 0.21 2.20 0.088
## [1041,] 7.4 0.965 0.00 2.20 0.088
## [1042,] 6.9 0.490 0.19 1.70 0.079
## [1043,] 8.9 0.500 0.21 2.20 0.088
## [1044,] 9.5 0.390 0.41 8.90 0.069
## [1045,] 6.4 0.390 0.33 3.30 0.046
## [1046,] 6.9 0.440 0.00 1.40 0.070
## [1047,] 7.6 0.780 0.00 1.70 0.076
## [1048,] 7.1 0.430 0.17 1.80 0.082
## [1049,] 9.3 0.490 0.36 1.70 0.081
## [1050,] 9.3 0.500 0.36 1.80 0.084
## [1051,] 7.1 0.430 0.17 1.80 0.082
## [1052,] 8.5 0.460 0.59 1.40 0.414
## [1053,] 5.6 0.605 0.05 2.40 0.073
## [1054,] 8.3 0.330 0.42 2.30 0.070
## [1055,] 8.2 0.640 0.27 2.00 0.095
## [1056,] 8.2 0.640 0.27 2.00 0.095
## [1057,] 8.9 0.480 0.53 4.00 0.101
## [1058,] 7.6 0.420 0.25 3.90 0.104
## [1059,] 9.9 0.530 0.57 2.40 0.093
## [1060,] 8.9 0.480 0.53 4.00 0.101
## [1061,] 11.6 0.230 0.57 1.80 0.074
## [1062,] 9.1 0.400 0.50 1.80 0.071
## [1063,] 8.0 0.380 0.44 1.90 0.098
## [1064,] 10.2 0.290 0.65 2.40 0.075
## [1065,] 8.2 0.740 0.09 2.00 0.067
## [1066,] 7.7 0.610 0.18 2.40 0.083
## [1067,] 6.6 0.520 0.08 2.40 0.070
## [1068,] 11.1 0.310 0.53 2.20 0.060
## [1069,] 11.1 0.310 0.53 2.20 0.060
## [1070,] 8.0 0.620 0.35 2.80 0.086
## [1071,] 9.3 0.330 0.45 1.50 0.057
## [1072,] 7.5 0.770 0.20 8.10 0.098
## [1073,] 7.2 0.350 0.26 1.80 0.083
## [1074,] 8.0 0.620 0.33 2.70 0.088
## [1075,] 7.5 0.770 0.20 8.10 0.098
## [1076,] 9.1 0.250 0.34 2.00 0.071
## [1077,] 9.9 0.320 0.56 2.00 0.073
## [1078,] 8.6 0.370 0.65 6.40 0.080
## [1079,] 8.6 0.370 0.65 6.40 0.080
## [1080,] 7.9 0.300 0.68 8.30 0.050
## [1081,] 10.3 0.270 0.56 1.40 0.047
## [1082,] 7.9 0.300 0.68 8.30 0.050
## [1083,] 7.2 0.380 0.30 1.80 0.073
## [1084,] 8.7 0.420 0.45 2.40 0.072
## [1085,] 7.2 0.380 0.30 1.80 0.073
## [1086,] 6.8 0.480 0.08 1.80 0.074
## [1087,] 8.5 0.340 0.40 4.70 0.055
## [1088,] 7.9 0.190 0.42 1.60 0.057
## [1089,] 11.6 0.410 0.54 1.50 0.095
## [1090,] 11.6 0.410 0.54 1.50 0.095
## [1091,] 10.0 0.260 0.54 1.90 0.083
## [1092,] 7.9 0.340 0.42 2.00 0.086
## [1093,] 7.0 0.540 0.09 2.00 0.081
## [1094,] 9.2 0.310 0.36 2.20 0.079
## [1095,] 6.6 0.725 0.09 5.50 0.117
## [1096,] 9.4 0.400 0.47 2.50 0.087
## [1097,] 6.6 0.725 0.09 5.50 0.117
## [1098,] 8.6 0.520 0.38 1.50 0.096
## [1099,] 8.0 0.310 0.45 2.10 0.216
## [1100,] 8.6 0.520 0.38 1.50 0.096
## [1101,] 8.4 0.340 0.42 2.10 0.072
## [1102,] 7.4 0.490 0.27 2.10 0.071
## [1103,] 6.1 0.480 0.09 1.70 0.078
## [1104,] 7.4 0.490 0.27 2.10 0.071
## [1105,] 8.0 0.480 0.34 2.20 0.073
## [1106,] 6.3 0.570 0.28 2.10 0.048
## [1107,] 8.2 0.230 0.42 1.90 0.069
## [1108,] 9.1 0.300 0.41 2.00 0.068
## [1109,] 8.1 0.780 0.10 3.30 0.090
## [1110,] 10.8 0.470 0.43 2.10 0.171
## [1111,] 8.3 0.530 0.00 1.40 0.070
## [1112,] 5.4 0.420 0.27 2.00 0.092
## [1113,] 7.9 0.330 0.41 1.50 0.056
## [1114,] 8.9 0.240 0.39 1.60 0.074
## [1115,] 5.0 0.400 0.50 4.30 0.046
## [1116,] 7.0 0.690 0.07 2.50 0.091
## [1117,] 7.0 0.690 0.07 2.50 0.091
## [1118,] 7.0 0.690 0.07 2.50 0.091
## [1119,] 7.1 0.390 0.12 2.10 0.065
## [1120,] 5.6 0.660 0.00 2.50 0.066
## [1121,] 7.9 0.540 0.34 2.50 0.076
## [1122,] 6.6 0.500 0.00 1.80 0.062
## [1123,] 6.3 0.470 0.00 1.40 0.055
## [1124,] 10.7 0.400 0.37 1.90 0.081
## [1125,] 6.5 0.580 0.00 2.20 0.096
## [1126,] 8.8 0.240 0.35 1.70 0.055
## [1127,] 5.8 0.290 0.26 1.70 0.063
## [1128,] 6.3 0.760 0.00 2.90 0.072
## [1129,] 10.0 0.430 0.33 2.70 0.095
## [1130,] 10.5 0.430 0.35 3.30 0.092
## [1131,] 9.1 0.600 0.00 1.90 0.058
## [1132,] 5.9 0.190 0.21 1.70 0.045
## [1133,] 7.4 0.360 0.34 1.80 0.075
## [1134,] 7.2 0.480 0.07 5.50 0.089
## [1135,] 8.5 0.280 0.35 1.70 0.061
## [1136,] 8.0 0.250 0.43 1.70 0.067
## [1137,] 10.4 0.520 0.45 2.00 0.080
## [1138,] 10.4 0.520 0.45 2.00 0.080
## [1139,] 7.5 0.410 0.15 3.70 0.104
## [1140,] 8.2 0.510 0.24 2.00 0.079
## [1141,] 7.3 0.400 0.30 1.70 0.080
## [1142,] 8.2 0.380 0.32 2.50 0.080
## [1143,] 6.9 0.450 0.11 2.40 0.043
## [1144,] 7.0 0.220 0.30 1.80 0.065
## [1145,] 7.3 0.320 0.23 2.30 0.066
## [1146,] 8.2 0.200 0.43 2.50 0.076
## [1147,] 7.8 0.500 0.12 1.80 0.178
## [1148,] 10.0 0.410 0.45 6.20 0.071
## [1149,] 7.8 0.390 0.42 2.00 0.086
## [1150,] 10.0 0.350 0.47 2.00 0.061
## [1151,] 8.2 0.330 0.32 2.80 0.067
## [1152,] 6.1 0.580 0.23 2.50 0.044
## [1153,] 8.3 0.600 0.25 2.20 0.118
## [1154,] 9.6 0.420 0.35 2.10 0.083
## [1155,] 6.6 0.580 0.00 2.20 0.100
## [1156,] 8.3 0.600 0.25 2.20 0.118
## [1157,] 8.5 0.180 0.51 1.75 0.071
## [1158,] 5.1 0.510 0.18 2.10 0.042
## [1159,] 6.7 0.410 0.43 2.80 0.076
## [1160,] 10.2 0.410 0.43 2.20 0.110
## [1161,] 10.6 0.360 0.57 2.30 0.087
## [1162,] 8.8 0.450 0.43 1.40 0.076
## [1163,] 8.5 0.320 0.42 2.30 0.075
## [1164,] 9.0 0.785 0.24 1.70 0.078
## [1165,] 9.0 0.785 0.24 1.70 0.078
## [1166,] 8.5 0.440 0.50 1.90 0.369
## [1167,] 9.9 0.540 0.26 2.00 0.111
## [1168,] 8.2 0.330 0.39 2.50 0.074
## [1169,] 6.5 0.340 0.27 2.80 0.067
## [1170,] 7.6 0.500 0.29 2.30 0.086
## [1171,] 9.2 0.360 0.34 1.60 0.062
## [1172,] 7.1 0.590 0.00 2.20 0.078
## [1173,] 9.7 0.420 0.46 2.10 0.074
## [1174,] 7.6 0.360 0.31 1.70 0.079
## [1175,] 7.6 0.360 0.31 1.70 0.079
## [1176,] 6.5 0.610 0.00 2.20 0.095
## [1177,] 6.5 0.880 0.03 5.60 0.079
## [1178,] 7.1 0.660 0.00 2.40 0.052
## [1179,] 5.6 0.915 0.00 2.10 0.041
## [1180,] 8.2 0.350 0.33 2.40 0.076
## [1181,] 8.2 0.350 0.33 2.40 0.076
## [1182,] 9.8 0.390 0.43 1.65 0.068
## [1183,] 10.2 0.400 0.40 2.50 0.068
## [1184,] 6.8 0.660 0.07 1.60 0.070
## [1185,] 6.7 0.640 0.23 2.10 0.080
## [1186,] 7.0 0.430 0.30 2.00 0.085
## [1187,] 6.6 0.800 0.03 7.80 0.079
## [1188,] 7.0 0.430 0.30 2.00 0.085
## [1189,] 6.7 0.640 0.23 2.10 0.080
## [1190,] 8.8 0.955 0.05 1.80 0.075
## [1191,] 9.1 0.400 0.57 4.60 0.080
## [1192,] 6.5 0.885 0.00 2.30 0.166
## [1193,] 7.2 0.250 0.37 2.50 0.063
## [1194,] 6.4 0.885 0.00 2.30 0.166
## [1195,] 7.0 0.745 0.12 1.80 0.114
## [1196,] 6.2 0.430 0.22 1.80 0.078
## [1197,] 7.9 0.580 0.23 2.30 0.076
## [1198,] 7.7 0.570 0.21 1.50 0.069
## [1199,] 7.7 0.260 0.26 2.00 0.052
## [1200,] 7.9 0.580 0.23 2.30 0.076
## [1201,] 7.7 0.570 0.21 1.50 0.069
## [1202,] 7.9 0.340 0.36 1.90 0.065
## [1203,] 8.6 0.420 0.39 1.80 0.068
## [1204,] 9.9 0.740 0.19 5.80 0.111
## [1205,] 7.2 0.360 0.46 2.10 0.074
## [1206,] 7.2 0.360 0.46 2.10 0.074
## [1207,] 7.2 0.360 0.46 2.10 0.074
## [1208,] 9.9 0.720 0.55 1.70 0.136
## [1209,] 7.2 0.360 0.46 2.10 0.074
## [1210,] 6.2 0.390 0.43 2.00 0.071
## [1211,] 6.8 0.650 0.02 2.10 0.078
## [1212,] 6.6 0.440 0.15 2.10 0.076
## [1213,] 6.8 0.650 0.02 2.10 0.078
## [1214,] 9.6 0.380 0.42 1.90 0.071
## [1215,] 10.2 0.330 0.46 1.90 0.081
## [1216,] 8.8 0.270 0.46 2.10 0.095
## [1217,] 7.9 0.570 0.31 2.00 0.079
## [1218,] 8.2 0.340 0.37 1.90 0.057
## [1219,] 8.2 0.400 0.31 1.90 0.082
## [1220,] 9.0 0.390 0.40 1.30 0.044
## [1221,] 10.9 0.320 0.52 1.80 0.132
## [1222,] 10.9 0.320 0.52 1.80 0.132
## [1223,] 8.1 0.530 0.22 2.20 0.078
## [1224,] 10.5 0.360 0.47 2.20 0.074
## [1225,] 12.6 0.390 0.49 2.50 0.080
## [1226,] 9.2 0.460 0.23 2.60 0.091
## [1227,] 7.5 0.580 0.03 4.10 0.080
## [1228,] 9.0 0.580 0.25 2.00 0.104
## [1229,] 5.1 0.420 0.00 1.80 0.044
## [1230,] 7.6 0.430 0.29 2.10 0.075
## [1231,] 7.7 0.180 0.34 2.70 0.066
## [1232,] 7.8 0.815 0.01 2.60 0.074
## [1233,] 7.6 0.430 0.29 2.10 0.075
## [1234,] 10.2 0.230 0.37 2.20 0.057
## [1235,] 7.1 0.750 0.01 2.20 0.059
## [1236,] 6.0 0.330 0.32 12.90 0.054
## [1237,] 7.8 0.550 0.00 1.70 0.070
## [1238,] 7.1 0.750 0.01 2.20 0.059
## [1239,] 8.1 0.730 0.00 2.50 0.081
## [1240,] 6.5 0.670 0.00 4.30 0.057
## [1241,] 7.5 0.610 0.20 1.70 0.076
## [1242,] 9.8 0.370 0.39 2.50 0.079
## [1243,] 9.0 0.400 0.41 2.00 0.058
## [1244,] 8.3 0.560 0.22 2.40 0.082
## [1245,] 5.9 0.290 0.25 13.40 0.067
## [1246,] 7.4 0.550 0.19 1.80 0.082
## [1247,] 7.4 0.740 0.07 1.70 0.086
## [1248,] 7.4 0.550 0.19 1.80 0.082
## [1249,] 6.9 0.410 0.33 2.20 0.081
## [1250,] 7.1 0.600 0.01 2.30 0.079
## [1251,] 7.1 0.600 0.01 2.30 0.079
## [1252,] 7.5 0.580 0.14 2.20 0.077
## [1253,] 7.1 0.720 0.00 1.80 0.123
## [1254,] 7.9 0.660 0.00 1.40 0.096
## [1255,] 7.8 0.700 0.06 1.90 0.079
## [1256,] 6.1 0.640 0.02 2.40 0.069
## [1257,] 7.5 0.590 0.22 1.80 0.082
## [1258,] 7.0 0.580 0.28 4.80 0.085
## [1259,] 6.8 0.640 0.00 2.70 0.123
## [1260,] 6.8 0.640 0.00 2.70 0.123
## [1261,] 8.6 0.635 0.68 1.80 0.403
## [1262,] 6.3 1.020 0.00 2.00 0.083
## [1263,] 9.8 0.450 0.38 2.50 0.081
## [1264,] 8.2 0.780 0.00 2.20 0.089
## [1265,] 8.5 0.370 0.32 1.80 0.066
## [1266,] 7.2 0.570 0.05 2.30 0.081
## [1267,] 7.2 0.570 0.05 2.30 0.081
## [1268,] 10.4 0.430 0.50 2.30 0.068
## [1269,] 6.9 0.410 0.31 2.00 0.079
## [1270,] 5.5 0.490 0.03 1.80 0.044
## [1271,] 5.0 0.380 0.01 1.60 0.048
## [1272,] 7.3 0.440 0.20 1.60 0.049
## [1273,] 5.9 0.460 0.00 1.90 0.077
## [1274,] 7.5 0.580 0.20 2.00 0.073
## [1275,] 7.8 0.580 0.13 2.10 0.102
## [1276,] 8.0 0.715 0.22 2.30 0.075
## [1277,] 8.5 0.400 0.40 6.30 0.050
## [1278,] 7.0 0.690 0.00 1.90 0.114
## [1279,] 8.0 0.715 0.22 2.30 0.075
## [1280,] 9.8 0.300 0.39 1.70 0.062
## [1281,] 7.1 0.460 0.20 1.90 0.077
## [1282,] 7.1 0.460 0.20 1.90 0.077
## [1283,] 7.9 0.765 0.00 2.00 0.084
## [1284,] 8.7 0.630 0.28 2.70 0.096
## [1285,] 7.0 0.420 0.19 2.30 0.071
## [1286,] 11.3 0.370 0.50 1.80 0.090
## [1287,] 7.1 0.160 0.44 2.50 0.068
## [1288,] 8.0 0.600 0.08 2.60 0.056
## [1289,] 7.0 0.600 0.30 4.50 0.068
## [1290,] 7.0 0.600 0.30 4.50 0.068
## [1291,] 7.6 0.740 0.00 1.90 0.100
## [1292,] 8.2 0.635 0.10 2.10 0.073
## [1293,] 5.9 0.395 0.13 2.40 0.056
## [1294,] 7.5 0.755 0.00 1.90 0.084
## [1295,] 8.2 0.635 0.10 2.10 0.073
## [1296,] 6.6 0.630 0.00 4.30 0.093
## [1297,] 6.6 0.630 0.00 4.30 0.093
## [1298,] 7.2 0.530 0.14 2.10 0.064
## [1299,] 5.7 0.600 0.00 1.40 0.063
## [1300,] 7.6 1.580 0.00 2.10 0.137
## [1301,] 5.2 0.645 0.00 2.15 0.080
## [1302,] 6.7 0.860 0.07 2.00 0.100
## [1303,] 9.1 0.370 0.32 2.10 0.064
## [1304,] 8.0 0.280 0.44 1.80 0.081
## [1305,] 7.6 0.790 0.21 2.30 0.087
## [1306,] 7.5 0.610 0.26 1.90 0.073
## [1307,] 9.7 0.690 0.32 2.50 0.088
## [1308,] 6.8 0.680 0.09 3.90 0.068
## [1309,] 9.7 0.690 0.32 2.50 0.088
## [1310,] 7.0 0.620 0.10 1.40 0.071
## [1311,] 7.5 0.610 0.26 1.90 0.073
## [1312,] 6.5 0.510 0.15 3.00 0.064
## [1313,] 8.0 1.180 0.21 1.90 0.083
## [1314,] 7.0 0.360 0.21 2.30 0.086
## [1315,] 7.0 0.360 0.21 2.40 0.086
## [1316,] 7.5 0.630 0.27 2.00 0.083
## [1317,] 5.4 0.740 0.00 1.20 0.041
## [1318,] 9.9 0.440 0.46 2.20 0.091
## [1319,] 7.5 0.630 0.27 2.00 0.083
## [1320,] 9.1 0.760 0.68 1.70 0.414
## [1321,] 9.7 0.660 0.34 2.60 0.094
## [1322,] 5.0 0.740 0.00 1.20 0.041
## [1323,] 9.1 0.340 0.42 1.80 0.058
## [1324,] 9.1 0.360 0.39 1.80 0.060
## [1325,] 6.7 0.460 0.24 1.70 0.077
## [1326,] 6.7 0.460 0.24 1.70 0.077
## [1327,] 6.7 0.460 0.24 1.70 0.077
## [1328,] 6.7 0.460 0.24 1.70 0.077
## [1329,] 6.5 0.520 0.11 1.80 0.073
## [1330,] 7.4 0.600 0.26 2.10 0.083
## [1331,] 7.4 0.600 0.26 2.10 0.083
## [1332,] 7.8 0.870 0.26 3.80 0.107
## [1333,] 8.4 0.390 0.10 1.70 0.075
## [1334,] 9.1 0.775 0.22 2.20 0.079
## [1335,] 7.2 0.835 0.00 2.00 0.166
## [1336,] 6.6 0.580 0.02 2.40 0.069
## [1337,] 6.0 0.500 0.00 1.40 0.057
## [1338,] 6.0 0.500 0.00 1.40 0.057
## [1339,] 6.0 0.500 0.00 1.40 0.057
## [1340,] 7.5 0.510 0.02 1.70 0.084
## [1341,] 7.5 0.510 0.02 1.70 0.084
## [1342,] 7.5 0.510 0.02 1.70 0.084
## [1343,] 7.6 0.540 0.02 1.70 0.085
## [1344,] 7.5 0.510 0.02 1.70 0.084
## [1345,] 11.5 0.420 0.48 2.60 0.077
## [1346,] 8.2 0.440 0.24 2.30 0.063
## [1347,] 6.1 0.590 0.01 2.10 0.056
## [1348,] 7.2 0.655 0.03 1.80 0.078
## [1349,] 7.2 0.655 0.03 1.80 0.078
## [1350,] 6.9 0.570 0.00 2.80 0.081
## [1351,] 9.0 0.600 0.29 2.00 0.069
## [1352,] 7.2 0.620 0.01 2.30 0.065
## [1353,] 7.6 0.645 0.03 1.90 0.086
## [1354,] 7.6 0.645 0.03 1.90 0.086
## [1355,] 7.2 0.580 0.03 2.30 0.077
## [1356,] 6.1 0.320 0.25 1.80 0.086
## [1357,] 6.1 0.340 0.25 1.80 0.084
## [1358,] 7.3 0.430 0.24 2.50 0.078
## [1359,] 7.4 0.640 0.17 5.40 0.168
## [1360,] 11.6 0.475 0.40 1.40 0.091
## [1361,] 9.2 0.540 0.31 2.30 0.112
## [1362,] 8.3 0.850 0.14 2.50 0.093
## [1363,] 11.6 0.475 0.40 1.40 0.091
## [1364,] 8.0 0.830 0.27 2.00 0.080
## [1365,] 7.2 0.605 0.02 1.90 0.096
## [1366,] 7.8 0.500 0.09 2.20 0.115
## [1367,] 7.3 0.740 0.08 1.70 0.094
## [1368,] 6.9 0.540 0.30 2.20 0.088
## [1369,] 8.0 0.770 0.32 2.10 0.079
## [1370,] 6.6 0.610 0.00 1.60 0.069
## [1371,] 8.7 0.780 0.51 1.70 0.415
## [1372,] 7.5 0.580 0.56 3.10 0.153
## [1373,] 8.7 0.780 0.51 1.70 0.415
## [1374,] 7.7 0.750 0.27 3.80 0.110
## [1375,] 6.8 0.815 0.00 1.20 0.267
## [1376,] 7.2 0.560 0.26 2.00 0.083
## [1377,] 8.2 0.885 0.20 1.40 0.086
## [1378,] 5.2 0.490 0.26 2.30 0.090
## [1379,] 7.2 0.450 0.15 2.00 0.078
## [1380,] 7.5 0.570 0.02 2.60 0.077
## [1381,] 7.5 0.570 0.02 2.60 0.077
## [1382,] 6.8 0.830 0.09 1.80 0.074
## [1383,] 8.0 0.600 0.22 2.10 0.080
## [1384,] 8.0 0.600 0.22 2.10 0.080
## [1385,] 7.1 0.755 0.15 1.80 0.107
## [1386,] 8.0 0.810 0.25 3.40 0.076
## [1387,] 7.4 0.640 0.07 1.80 0.100
## [1388,] 7.4 0.640 0.07 1.80 0.100
## [1389,] 6.6 0.640 0.31 6.10 0.083
## [1390,] 6.7 0.480 0.02 2.20 0.080
## [1391,] 6.0 0.490 0.00 2.30 0.068
## [1392,] 8.0 0.640 0.22 2.40 0.094
## [1393,] 7.1 0.620 0.06 1.30 0.070
## [1394,] 8.0 0.520 0.25 2.00 0.078
## [1395,] 6.4 0.570 0.14 3.90 0.070
## [1396,] 8.6 0.685 0.10 1.60 0.092
## [1397,] 8.7 0.675 0.10 1.60 0.090
## [1398,] 7.3 0.590 0.26 2.00 0.080
## [1399,] 7.0 0.600 0.12 2.20 0.083
## [1400,] 7.2 0.670 0.00 2.20 0.068
## [1401,] 7.9 0.690 0.21 2.10 0.080
## [1402,] 7.9 0.690 0.21 2.10 0.080
## [1403,] 7.6 0.300 0.42 2.00 0.052
## [1404,] 7.2 0.330 0.33 1.70 0.061
## [1405,] 8.0 0.500 0.39 2.60 0.082
## [1406,] 7.7 0.280 0.30 2.00 0.062
## [1407,] 8.2 0.240 0.34 5.10 0.062
## [1408,] 6.0 0.510 0.00 2.10 0.064
## [1409,] 8.1 0.290 0.36 2.20 0.048
## [1410,] 6.0 0.510 0.00 2.10 0.064
## [1411,] 6.6 0.960 0.00 1.80 0.082
## [1412,] 6.4 0.470 0.40 2.40 0.071
## [1413,] 8.2 0.240 0.34 5.10 0.062
## [1414,] 9.9 0.570 0.25 2.00 0.104
## [1415,] 10.0 0.320 0.59 2.20 0.077
## [1416,] 6.2 0.580 0.00 1.60 0.065
## [1417,] 10.0 0.320 0.59 2.20 0.077
## [1418,] 7.3 0.340 0.33 2.50 0.064
## [1419,] 7.8 0.530 0.01 1.60 0.077
## [1420,] 7.7 0.640 0.21 2.20 0.077
## [1421,] 7.8 0.530 0.01 1.60 0.077
## [1422,] 7.5 0.400 0.18 1.60 0.079
## [1423,] 7.0 0.540 0.00 2.10 0.079
## [1424,] 6.4 0.530 0.09 3.90 0.123
## [1425,] 8.3 0.260 0.37 1.40 0.076
## [1426,] 8.3 0.260 0.37 1.40 0.076
## [1427,] 7.7 0.230 0.37 1.80 0.046
## [1428,] 7.6 0.410 0.33 2.50 0.078
## [1429,] 7.8 0.640 0.00 1.90 0.072
## [1430,] 7.9 0.180 0.40 2.20 0.049
## [1431,] 7.4 0.410 0.24 1.80 0.066
## [1432,] 7.6 0.430 0.31 2.10 0.069
## [1433,] 5.9 0.440 0.00 1.60 0.042
## [1434,] 6.1 0.400 0.16 1.80 0.069
## [1435,] 10.2 0.540 0.37 15.40 0.214
## [1436,] 10.2 0.540 0.37 15.40 0.214
## [1437,] 10.0 0.380 0.38 1.60 0.169
## [1438,] 6.8 0.915 0.29 4.80 0.070
## [1439,] 7.0 0.590 0.00 1.70 0.052
## [1440,] 7.3 0.670 0.02 2.20 0.072
## [1441,] 7.2 0.370 0.32 2.00 0.062
## [1442,] 7.4 0.785 0.19 5.20 0.094
## [1443,] 6.9 0.630 0.02 1.90 0.078
## [1444,] 6.9 0.580 0.20 1.75 0.058
## [1445,] 7.3 0.670 0.02 2.20 0.072
## [1446,] 7.4 0.785 0.19 5.20 0.094
## [1447,] 6.9 0.630 0.02 1.90 0.078
## [1448,] 6.8 0.670 0.00 1.90 0.080
## [1449,] 6.9 0.580 0.01 1.90 0.080
## [1450,] 7.2 0.380 0.31 2.00 0.056
## [1451,] 7.2 0.370 0.32 2.00 0.062
## [1452,] 7.8 0.320 0.44 2.70 0.104
## [1453,] 6.6 0.580 0.02 2.00 0.062
## [1454,] 7.6 0.490 0.33 1.90 0.074
## [1455,] 11.7 0.450 0.63 2.20 0.073
## [1456,] 6.5 0.900 0.00 1.60 0.052
## [1457,] 6.0 0.540 0.06 1.80 0.050
## [1458,] 7.6 0.490 0.33 1.90 0.074
## [1459,] 8.4 0.290 0.40 1.70 0.067
## [1460,] 7.9 0.200 0.35 1.70 0.054
## [1461,] 6.4 0.420 0.09 2.30 0.054
## [1462,] 6.2 0.785 0.00 2.10 0.060
## [1463,] 6.8 0.640 0.03 2.30 0.075
## [1464,] 6.9 0.630 0.01 2.40 0.076
## [1465,] 6.8 0.590 0.10 1.70 0.063
## [1466,] 6.8 0.590 0.10 1.70 0.063
## [1467,] 7.3 0.480 0.32 2.10 0.062
## [1468,] 6.7 1.040 0.08 2.30 0.067
## [1469,] 7.3 0.480 0.32 2.10 0.062
## [1470,] 7.3 0.980 0.05 2.10 0.061
## [1471,] 10.0 0.690 0.11 1.40 0.084
## [1472,] 6.7 0.700 0.08 3.75 0.067
## [1473,] 7.6 0.350 0.60 2.60 0.073
## [1474,] 6.1 0.600 0.08 1.80 0.071
## [1475,] 9.9 0.500 0.50 13.80 0.205
## [1476,] 5.3 0.470 0.11 2.20 0.048
## [1477,] 9.9 0.500 0.50 13.80 0.205
## [1478,] 5.3 0.470 0.11 2.20 0.048
## [1479,] 7.1 0.875 0.05 5.70 0.082
## [1480,] 8.2 0.280 0.60 3.00 0.104
## [1481,] 5.6 0.620 0.03 1.50 0.080
## [1482,] 8.2 0.280 0.60 3.00 0.104
## [1483,] 7.2 0.580 0.54 2.10 0.114
## [1484,] 8.1 0.330 0.44 1.50 0.042
## [1485,] 6.8 0.910 0.06 2.00 0.060
## [1486,] 7.0 0.655 0.16 2.10 0.074
## [1487,] 6.8 0.680 0.21 2.10 0.070
## [1488,] 6.0 0.640 0.05 1.90 0.066
## [1489,] 5.6 0.540 0.04 1.70 0.049
## [1490,] 6.2 0.570 0.10 2.10 0.048
## [1491,] 7.1 0.220 0.49 1.80 0.039
## [1492,] 5.6 0.540 0.04 1.70 0.049
## [1493,] 6.2 0.650 0.06 1.60 0.050
## [1494,] 7.7 0.540 0.26 1.90 0.089
## [1495,] 6.4 0.310 0.09 1.40 0.066
## [1496,] 7.0 0.430 0.02 1.90 0.080
## [1497,] 7.7 0.540 0.26 1.90 0.089
## [1498,] 6.9 0.740 0.03 2.30 0.054
## [1499,] 6.6 0.895 0.04 2.30 0.068
## [1500,] 6.9 0.740 0.03 2.30 0.054
## [1501,] 7.5 0.725 0.04 1.50 0.076
## [1502,] 7.8 0.820 0.29 4.30 0.083
## [1503,] 7.3 0.585 0.18 2.40 0.078
## [1504,] 6.2 0.440 0.39 2.50 0.077
## [1505,] 7.5 0.380 0.57 2.30 0.106
## [1506,] 6.7 0.760 0.02 1.80 0.078
## [1507,] 6.8 0.810 0.05 2.00 0.070
## [1508,] 7.5 0.380 0.57 2.30 0.106
## [1509,] 7.1 0.270 0.60 2.10 0.074
## [1510,] 7.9 0.180 0.40 1.80 0.062
## [1511,] 6.4 0.360 0.21 2.20 0.047
## [1512,] 7.1 0.690 0.04 2.10 0.068
## [1513,] 6.4 0.790 0.04 2.20 0.061
## [1514,] 6.4 0.560 0.15 1.80 0.078
## [1515,] 6.9 0.840 0.21 4.10 0.074
## [1516,] 6.9 0.840 0.21 4.10 0.074
## [1517,] 6.1 0.320 0.25 2.30 0.071
## [1518,] 6.5 0.530 0.06 2.00 0.063
## [1519,] 7.4 0.470 0.46 2.20 0.114
## [1520,] 6.6 0.700 0.08 2.60 0.106
## [1521,] 6.5 0.530 0.06 2.00 0.063
## [1522,] 6.9 0.480 0.20 1.90 0.082
## [1523,] 6.1 0.320 0.25 2.30 0.071
## [1524,] 6.8 0.480 0.25 2.00 0.076
## [1525,] 6.0 0.420 0.19 2.00 0.075
## [1526,] 6.7 0.480 0.08 2.10 0.064
## [1527,] 6.8 0.470 0.08 2.20 0.064
## [1528,] 7.1 0.530 0.07 1.70 0.071
## [1529,] 7.9 0.290 0.49 2.20 0.096
## [1530,] 7.1 0.690 0.08 2.10 0.063
## [1531,] 6.6 0.440 0.09 2.20 0.063
## [1532,] 6.1 0.705 0.10 2.80 0.081
## [1533,] 7.2 0.530 0.13 2.00 0.058
## [1534,] 8.0 0.390 0.30 1.90 0.074
## [1535,] 6.6 0.560 0.14 2.40 0.064
## [1536,] 7.0 0.550 0.13 2.20 0.075
## [1537,] 6.1 0.530 0.08 1.90 0.077
## [1538,] 5.4 0.580 0.08 1.90 0.059
## [1539,] 6.2 0.640 0.09 2.50 0.081
## [1540,] 7.2 0.390 0.32 1.80 0.065
## [1541,] 6.2 0.520 0.08 4.40 0.071
## [1542,] 7.4 0.250 0.29 2.20 0.054
## [1543,] 6.7 0.855 0.02 1.90 0.064
## [1544,] 11.1 0.440 0.42 2.20 0.064
## [1545,] 8.4 0.370 0.43 2.30 0.063
## [1546,] 6.5 0.630 0.33 1.80 0.059
## [1547,] 7.0 0.570 0.02 2.00 0.072
## [1548,] 6.3 0.600 0.10 1.60 0.048
## [1549,] 11.2 0.400 0.50 2.00 0.099
## [1550,] 7.4 0.360 0.30 1.80 0.074
## [1551,] 7.1 0.680 0.00 2.30 0.087
## [1552,] 7.1 0.670 0.00 2.30 0.083
## [1553,] 6.3 0.680 0.01 3.70 0.103
## [1554,] 7.3 0.735 0.00 2.20 0.080
## [1555,] 6.6 0.855 0.02 2.40 0.062
## [1556,] 7.0 0.560 0.17 1.70 0.065
## [1557,] 6.6 0.880 0.04 2.20 0.066
## [1558,] 6.6 0.855 0.02 2.40 0.062
## [1559,] 6.9 0.630 0.33 6.70 0.235
## [1560,] 7.8 0.600 0.26 2.00 0.080
## [1561,] 7.8 0.600 0.26 2.00 0.080
## [1562,] 7.8 0.600 0.26 2.00 0.080
## [1563,] 7.2 0.695 0.13 2.00 0.076
## [1564,] 7.2 0.695 0.13 2.00 0.076
## [1565,] 7.2 0.695 0.13 2.00 0.076
## [1566,] 6.7 0.670 0.02 1.90 0.061
## [1567,] 6.7 0.160 0.64 2.10 0.059
## [1568,] 7.2 0.695 0.13 2.00 0.076
## [1569,] 7.0 0.560 0.13 1.60 0.077
## [1570,] 6.2 0.510 0.14 1.90 0.056
## [1571,] 6.4 0.360 0.53 2.20 0.230
## [1572,] 6.4 0.380 0.14 2.20 0.038
## [1573,] 7.3 0.690 0.32 2.20 0.069
## [1574,] 6.0 0.580 0.20 2.40 0.075
## [1575,] 5.6 0.310 0.78 13.90 0.074
## [1576,] 7.5 0.520 0.40 2.20 0.060
## [1577,] 8.0 0.300 0.63 1.60 0.081
## [1578,] 6.2 0.700 0.15 5.10 0.076
## [1579,] 6.8 0.670 0.15 1.80 0.118
## [1580,] 6.2 0.560 0.09 1.70 0.053
## [1581,] 7.4 0.350 0.33 2.40 0.068
## [1582,] 6.2 0.560 0.09 1.70 0.053
## [1583,] 6.1 0.715 0.10 2.60 0.053
## [1584,] 6.2 0.460 0.29 2.10 0.074
## [1585,] 6.7 0.320 0.44 2.40 0.061
## [1586,] 7.2 0.390 0.44 2.60 0.066
## [1587,] 7.5 0.310 0.41 2.40 0.065
## [1588,] 5.8 0.610 0.11 1.80 0.066
## [1589,] 7.2 0.660 0.33 2.50 0.068
## [1590,] 6.6 0.725 0.20 7.80 0.073
## [1591,] 6.3 0.550 0.15 1.80 0.077
## [1592,] 5.4 0.740 0.09 1.70 0.089
## [1593,] 6.3 0.510 0.13 2.30 0.076
## [1594,] 6.8 0.620 0.08 1.90 0.068
## [1595,] 6.2 0.600 0.08 2.00 0.090
## [1596,] 5.9 0.550 0.10 2.20 0.062
## [1597,] 6.3 0.510 0.13 2.30 0.076
## [1598,] 5.9 0.645 0.12 2.00 0.075
## [1599,] 6.0 0.310 0.47 3.60 0.067
## free sulfur dioxide total sulfur dioxide density pH sulphates
## [1,] 11.0 34.0 0.99780 3.51 0.56
## [2,] 25.0 67.0 0.99680 3.20 0.68
## [3,] 15.0 54.0 0.99700 3.26 0.65
## [4,] 17.0 60.0 0.99800 3.16 0.58
## [5,] 11.0 34.0 0.99780 3.51 0.56
## [6,] 13.0 40.0 0.99780 3.51 0.56
## [7,] 15.0 59.0 0.99640 3.30 0.46
## [8,] 15.0 21.0 0.99460 3.39 0.47
## [9,] 9.0 18.0 0.99680 3.36 0.57
## [10,] 17.0 102.0 0.99780 3.35 0.80
## [11,] 15.0 65.0 0.99590 3.28 0.54
## [12,] 17.0 102.0 0.99780 3.35 0.80
## [13,] 16.0 59.0 0.99430 3.58 0.52
## [14,] 9.0 29.0 0.99740 3.26 1.56
## [15,] 52.0 145.0 0.99860 3.16 0.88
## [16,] 51.0 148.0 0.99860 3.17 0.93
## [17,] 35.0 103.0 0.99690 3.30 0.75
## [18,] 16.0 56.0 0.99680 3.11 1.28
## [19,] 6.0 29.0 0.99740 3.38 0.50
## [20,] 17.0 56.0 0.99690 3.04 1.08
## [21,] 29.0 60.0 0.99680 3.39 0.53
## [22,] 23.0 71.0 0.99820 3.52 0.65
## [23,] 10.0 37.0 0.99660 3.17 0.91
## [24,] 9.0 67.0 0.99680 3.17 0.53
## [25,] 21.0 40.0 0.99680 3.43 0.63
## [26,] 11.0 23.0 0.99550 3.34 0.56
## [27,] 4.0 11.0 0.99620 3.28 0.59
## [28,] 10.0 37.0 0.99660 3.17 0.91
## [29,] 14.0 35.0 0.99720 3.47 0.55
## [30,] 8.0 16.0 0.99640 3.38 0.59
## [31,] 17.0 82.0 0.99580 3.35 0.54
## [32,] 22.0 37.0 0.99660 3.46 0.57
## [33,] 15.0 113.0 0.99660 3.17 0.66
## [34,] 40.0 83.0 0.99930 3.45 0.52
## [35,] 13.0 50.0 0.99570 3.38 0.55
## [36,] 5.0 18.0 0.99860 3.40 0.55
## [37,] 3.0 15.0 0.99750 3.42 0.60
## [38,] 13.0 30.0 0.99680 3.23 0.73
## [39,] 7.0 19.0 0.99400 3.50 0.48
## [40,] 12.0 87.0 0.99780 3.33 0.83
## [41,] 12.0 87.0 0.99780 3.33 0.83
## [42,] 17.0 46.0 0.99760 3.26 0.51
## [43,] 8.0 14.0 0.99680 3.21 0.90
## [44,] 9.0 23.0 0.99680 3.30 1.20
## [45,] 5.0 11.0 0.99620 3.48 0.52
## [46,] 8.0 65.0 0.99340 3.90 0.56
## [47,] 22.0 114.0 0.99700 3.25 0.73
## [48,] 12.0 37.0 0.99690 3.25 0.58
## [49,] 5.0 12.0 0.99580 3.34 0.56
## [50,] 12.0 96.0 0.99540 3.32 0.58
## [51,] 4.0 23.0 0.99710 3.15 0.74
## [52,] 8.0 15.0 0.99560 3.40 0.63
## [53,] 6.0 14.0 0.99550 3.39 0.64
## [54,] 30.0 119.0 0.99700 3.20 0.56
## [55,] 33.0 73.0 0.99550 3.17 0.63
## [56,] 25.0 45.0 0.99780 3.34 0.53
## [57,] 4.0 10.0 0.99710 3.04 0.63
## [58,] 50.0 110.0 0.99830 3.26 0.77
## [59,] 17.0 54.0 0.99750 3.43 0.59
## [60,] 9.0 46.0 0.99620 3.41 0.54
## [61,] 19.0 52.0 0.99800 3.44 0.64
## [62,] 20.0 112.0 0.99680 3.21 0.71
## [63,] 12.0 35.0 0.99680 3.38 0.62
## [64,] 13.0 54.0 0.99660 3.39 0.57
## [65,] 4.0 11.0 0.99620 3.41 0.39
## [66,] 4.0 11.0 0.99620 3.41 0.39
## [67,] 11.0 39.0 0.99680 3.42 0.58
## [68,] 6.0 15.0 0.99620 3.44 0.58
## [69,] 27.0 65.0 0.99690 3.28 0.79
## [70,] 8.0 19.0 0.99620 3.34 0.95
## [71,] 15.0 27.0 0.99670 3.32 0.54
## [72,] 17.0 96.0 0.99620 3.32 0.48
## [73,] 18.0 94.0 0.99610 3.31 0.48
## [74,] 11.0 43.0 0.99760 3.31 0.53
## [75,] 28.0 83.0 0.99840 3.28 0.82
## [76,] 9.0 42.0 0.99860 3.54 0.66
## [77,] 9.0 42.0 0.99860 3.54 0.66
## [78,] 14.0 30.0 0.99660 3.52 0.55
## [79,] 12.0 80.0 0.99580 3.38 0.52
## [80,] 27.0 119.0 0.99720 3.16 1.12
## [81,] 3.0 15.0 0.99580 3.41 0.56
## [82,] 22.0 67.0 0.99740 3.13 1.28
## [83,] 21.0 73.0 0.99700 3.36 0.57
## [84,] 16.0 51.0 0.99690 3.16 1.14
## [85,] 18.0 61.0 0.99590 3.44 0.78
## [86,] 19.0 40.0 0.99610 3.41 0.59
## [87,] 20.0 136.0 0.99720 2.93 1.95
## [88,] 9.0 31.0 0.99660 3.39 0.64
## [89,] 34.0 125.0 0.99780 3.14 1.22
## [90,] 8.0 24.0 0.99780 3.48 0.53
## [91,] 42.0 140.0 0.99640 3.23 0.54
## [92,] 20.0 136.0 0.99720 2.93 1.95
## [93,] 19.0 133.0 0.99720 2.93 1.98
## [94,] 9.0 31.0 0.99660 3.39 0.64
## [95,] 41.0 85.0 0.99380 3.75 0.48
## [96,] 17.0 106.0 0.99320 3.85 0.60
## [97,] 8.0 23.0 0.99650 3.45 0.56
## [98,] 3.0 22.0 0.99630 3.25 0.63
## [99,] 5.0 10.0 0.99670 3.39 0.56
## [100,] 13.0 35.0 0.99720 3.30 0.59
## [101,] 11.0 50.0 0.99720 3.40 0.61
## [102,] 8.0 22.0 0.99590 3.31 0.56
## [103,] 13.0 35.0 0.99720 3.30 0.59
## [104,] 12.0 65.0 0.99670 3.29 0.51
## [105,] 5.0 36.0 0.99600 3.33 0.48
## [106,] 12.0 65.0 0.99670 3.29 0.51
## [107,] 18.0 69.0 0.99730 3.08 1.31
## [108,] 15.0 64.0 0.99690 3.46 0.79
## [109,] 18.0 80.0 0.99760 3.37 0.80
## [110,] 37.0 153.0 0.99690 3.21 0.69
## [111,] 12.0 47.0 0.99640 3.19 0.93
## [112,] 11.0 108.0 0.99640 3.15 0.66
## [113,] 14.0 111.0 0.99640 3.15 0.66
## [114,] 22.0 46.0 0.99880 3.32 0.67
## [115,] 12.0 47.0 0.99640 3.19 0.93
## [116,] 13.0 62.0 0.99660 3.07 0.63
## [117,] 11.0 40.0 0.99780 3.39 0.61
## [118,] 7.0 28.0 0.99700 3.37 0.50
## [119,] 14.0 56.0 0.99620 3.21 0.60
## [120,] 22.0 89.0 0.99590 3.34 0.54
## [121,] 10.0 89.0 0.99620 3.30 0.57
## [122,] 14.0 56.0 0.99620 3.21 0.60
## [123,] 3.0 13.0 0.99800 3.49 0.52
## [124,] 11.0 34.0 0.99760 3.44 0.53
## [125,] 21.0 102.0 0.99600 3.39 0.48
## [126,] 27.0 90.0 0.99840 3.16 0.70
## [127,] 3.0 12.0 0.99640 3.53 0.49
## [128,] 3.0 12.0 0.99640 3.54 0.48
## [129,] 3.0 16.0 0.99620 3.42 0.92
## [130,] 6.0 19.0 0.99550 3.42 0.57
## [131,] 30.0 134.0 0.99680 3.24 0.66
## [132,] 17.0 99.0 0.99370 3.63 0.63
## [133,] 17.0 99.0 0.99370 3.63 0.63
## [134,] 17.0 26.0 0.99520 3.40 0.58
## [135,] 13.0 29.0 0.99590 3.22 0.55
## [136,] 16.0 63.0 0.99650 3.19 0.82
## [137,] 10.0 52.0 0.99680 3.23 0.77
## [138,] 13.0 45.0 0.99720 3.48 0.64
## [139,] 15.0 105.0 0.99620 3.33 0.54
## [140,] 17.0 108.0 0.99620 3.32 0.54
## [141,] 16.0 63.0 0.99650 3.19 0.82
## [142,] 10.0 52.0 0.99680 3.23 0.77
## [143,] 27.0 63.0 0.99160 3.68 0.79
## [144,] 3.0 20.0 0.99540 3.34 0.58
## [145,] 27.0 63.0 0.99160 3.68 0.79
## [146,] 32.0 141.0 0.99680 3.17 0.62
## [147,] 21.0 94.0 0.99440 3.54 0.52
## [148,] 10.0 88.0 0.99680 3.11 0.80
## [149,] 12.0 30.0 0.99590 3.42 0.58
## [150,] 11.0 43.0 0.99750 3.53 0.61
## [151,] 5.0 11.0 0.99580 3.33 0.53
## [152,] 32.0 69.0 0.99960 2.74 2.00
## [153,] 25.0 99.0 0.99500 3.35 0.54
## [154,] 25.0 99.0 0.99500 3.35 0.54
## [155,] 29.0 129.0 0.99730 3.42 0.72
## [156,] 28.0 128.0 0.99730 3.42 0.71
## [157,] 29.0 129.0 0.99730 3.42 0.72
## [158,] 28.0 128.0 0.99730 3.42 0.71
## [159,] 12.0 22.0 0.99690 3.48 0.50
## [160,] 18.0 86.0 0.99680 3.59 0.57
## [161,] 7.0 20.0 0.99590 3.20 0.56
## [162,] 9.0 20.0 0.99650 3.17 1.08
## [163,] 17.0 31.0 0.99640 3.33 0.56
## [164,] 36.0 121.0 0.99820 3.37 0.49
## [165,] 35.0 121.0 0.99810 3.37 0.49
## [166,] 14.0 96.0 0.99610 3.19 0.62
## [167,] 18.0 101.0 0.99560 3.34 0.52
## [168,] 17.0 42.0 0.99560 3.37 0.48
## [169,] 11.0 44.0 0.99530 3.47 0.55
## [170,] 15.0 63.0 0.99640 3.00 1.59
## [171,] 4.0 8.0 0.99720 3.36 0.33
## [172,] 6.0 18.0 0.99720 3.29 0.61
## [173,] 6.0 18.0 0.99720 3.29 0.61
## [174,] 24.0 42.0 0.99610 3.42 0.57
## [175,] 7.0 35.0 0.99610 3.33 0.47
## [176,] 19.0 49.0 0.99580 3.35 0.78
## [177,] 7.0 35.0 0.99610 3.33 0.47
## [178,] 8.0 38.0 0.99720 3.58 0.61
## [179,] 7.0 20.0 0.99690 3.48 0.56
## [180,] 10.0 42.0 0.99690 3.19 0.59
## [181,] 10.0 42.0 0.99690 3.19 0.59
## [182,] 23.0 110.0 0.99720 3.12 1.02
## [183,] 16.0 42.0 0.99720 3.44 0.52
## [184,] 11.0 65.0 0.99710 3.54 0.58
## [185,] 8.0 62.0 0.99700 3.52 0.58
## [186,] 26.0 85.0 0.99710 3.26 0.53
## [187,] 14.0 67.0 0.99720 3.34 0.55
## [188,] 9.0 26.0 0.99760 3.39 0.49
## [189,] 15.0 143.0 0.99680 3.20 0.55
## [190,] 17.0 144.0 0.99680 3.20 0.55
## [191,] 21.0 127.0 0.99760 3.23 0.62
## [192,] 21.0 49.0 0.99740 3.57 0.62
## [193,] 16.0 126.0 0.99690 3.28 0.61
## [194,] 7.0 28.0 0.99640 3.28 0.55
## [195,] 7.0 28.0 0.99640 3.28 0.55
## [196,] 24.0 120.0 0.99680 3.25 0.54
## [197,] 15.0 55.0 0.99680 3.46 0.59
## [198,] 12.0 27.0 0.99810 3.11 0.97
## [199,] 13.0 93.0 0.99240 3.57 0.85
## [200,] 12.0 31.0 0.99480 3.51 0.43
## [201,] 9.0 24.0 0.99695 3.22 0.82
## [202,] 39.0 145.0 0.99750 3.04 1.03
## [203,] 16.0 49.0 0.99545 3.36 0.79
## [204,] 16.0 39.0 0.99610 3.34 0.55
## [205,] 14.0 37.0 0.99615 3.34 0.56
## [206,] 9.0 28.0 0.99940 3.20 0.77
## [207,] 9.0 28.0 0.99940 3.20 0.77
## [208,] 26.0 120.0 0.99625 3.29 0.53
## [209,] 18.0 95.0 0.99660 3.22 0.67
## [210,] 7.0 19.0 0.99800 3.31 0.88
## [211,] 5.0 19.0 0.99585 3.30 0.86
## [212,] 10.0 62.0 0.99685 3.35 0.56
## [213,] 5.0 15.0 0.99800 3.21 0.67
## [214,] 28.0 65.0 0.99590 3.30 0.43
## [215,] 10.0 41.0 0.99740 3.24 0.71
## [216,] 26.0 121.0 0.99740 3.34 0.76
## [217,] 13.0 49.0 0.99620 3.14 0.57
## [218,] 11.0 41.0 0.99670 3.36 0.55
## [219,] 10.0 44.0 0.99570 3.39 0.54
## [220,] 24.0 144.0 0.99655 3.30 0.60
## [221,] 24.0 58.0 0.99640 3.34 0.59
## [222,] 16.0 72.0 0.99570 3.15 0.57
## [223,] 5.0 10.0 0.99525 3.42 0.60
## [224,] 8.0 28.0 0.99815 3.28 0.60
## [225,] 15.0 55.0 0.99745 3.31 0.57
## [226,] 29.0 63.0 0.99615 3.37 0.58
## [227,] 27.0 81.0 0.99640 3.04 1.61
## [228,] 9.0 23.0 0.99840 3.39 0.63
## [229,] 29.0 63.0 0.99615 3.37 0.58
## [230,] 10.0 37.0 0.99685 3.46 0.50
## [231,] 19.0 106.0 0.99270 3.54 0.62
## [232,] 12.0 49.0 0.99625 3.37 0.52
## [233,] 18.0 58.0 0.99800 3.34 0.70
## [234,] 10.0 37.0 0.99685 3.46 0.50
## [235,] 7.0 37.0 0.99685 3.32 0.55
## [236,] 14.0 38.0 0.99675 3.37 0.58
## [237,] 14.0 38.0 0.99675 3.37 0.58
## [238,] 15.0 39.0 0.99675 3.37 0.58
## [239,] 14.0 38.0 0.99675 3.37 0.58
## [240,] 7.0 37.0 0.99685 3.32 0.55
## [241,] 5.0 62.0 0.99710 3.00 1.09
## [242,] 6.0 24.0 0.99925 3.14 0.71
## [243,] 28.0 109.0 0.99565 3.08 0.49
## [244,] 10.0 24.0 1.00005 3.07 0.84
## [245,] 10.0 24.0 1.00005 3.07 0.84
## [246,] 6.0 23.0 0.99830 3.61 0.96
## [247,] 16.0 51.0 0.99685 3.38 0.52
## [248,] 11.0 73.0 0.99630 3.20 0.45
## [249,] 9.0 39.0 0.99615 3.35 0.48
## [250,] 6.0 23.0 0.99830 3.61 0.96
## [251,] 16.0 37.0 0.99850 3.22 0.78
## [252,] 16.0 34.0 0.99720 3.47 0.70
## [253,] 5.0 21.0 0.99860 3.17 0.53
## [254,] 8.0 86.0 0.99590 3.23 0.59
## [255,] 16.0 34.0 0.99720 3.47 0.70
## [256,] 17.0 119.0 0.99675 3.26 0.57
## [257,] 5.0 13.0 0.99800 3.22 0.62
## [258,] 12.0 56.0 0.99680 3.49 0.67
## [259,] 8.0 45.0 0.99680 3.06 1.26
## [260,] 14.0 33.0 0.99965 3.36 0.80
## [261,] 18.0 45.0 0.99625 3.29 0.65
## [262,] 12.0 67.0 0.99565 3.35 0.60
## [263,] 10.0 35.0 0.99575 3.34 0.57
## [264,] 23.0 49.0 0.99630 3.28 0.67
## [265,] 5.0 27.0 0.99990 3.08 0.87
## [266,] 6.0 10.0 0.99680 3.20 0.72
## [267,] 26.0 61.0 1.00025 3.60 0.72
## [268,] 15.0 37.0 0.99730 3.35 0.86
## [269,] 7.0 27.0 0.99870 3.69 0.91
## [270,] 4.0 23.0 0.99960 3.28 0.97
## [271,] 27.0 61.0 0.99650 3.36 0.67
## [272,] 4.0 23.0 0.99960 3.28 0.97
## [273,] 17.0 65.0 0.99935 3.22 0.78
## [274,] 10.0 38.0 0.99735 3.31 0.64
## [275,] 27.0 94.0 0.99915 3.38 0.77
## [276,] 27.0 61.0 0.99650 3.36 0.67
## [277,] 7.0 27.0 0.99870 3.69 0.91
## [278,] 4.0 23.0 0.99960 3.28 0.97
## [279,] 5.0 13.0 0.99760 3.23 0.82
## [280,] 10.0 47.0 0.99910 3.38 0.77
## [281,] 6.0 19.0 0.99860 3.12 0.82
## [282,] 5.0 10.0 0.99720 3.25 1.08
## [283,] 12.0 53.0 0.99710 3.36 0.57
## [284,] 10.0 47.0 0.99910 3.38 0.77
## [285,] 32.0 71.0 1.00015 3.31 0.71
## [286,] 32.0 71.0 1.00015 3.31 0.71
## [287,] 25.0 49.0 0.99970 3.10 0.76
## [288,] 29.0 53.0 0.99670 3.37 0.70
## [289,] 20.0 49.0 0.99760 3.34 0.86
## [290,] 33.0 98.0 1.00100 3.20 0.95
## [291,] 20.0 49.0 0.99760 3.34 0.86
## [292,] 6.0 18.0 0.99790 3.30 0.71
## [293,] 18.0 48.0 0.99940 3.22 0.64
## [294,] 5.0 16.0 0.99640 3.41 0.60
## [295,] 17.0 53.0 1.00140 3.05 0.81
## [296,] 5.0 27.0 1.00010 3.05 0.64
## [297,] 26.0 70.0 0.99810 3.16 0.52
## [298,] 8.0 29.0 0.99855 3.67 0.73
## [299,] 5.0 31.0 0.99930 3.67 0.80
## [300,] 8.0 33.0 0.99845 3.68 0.71
## [301,] 20.0 44.0 0.99650 3.38 0.59
## [302,] 7.0 15.0 0.99730 3.22 0.64
## [303,] 12.0 28.0 0.99940 3.51 0.72
## [304,] 5.0 21.0 0.99600 3.39 0.54
## [305,] 12.0 90.0 0.99730 3.20 0.52
## [306,] 6.0 25.0 0.99980 3.12 0.59
## [307,] 7.0 54.0 0.99660 3.36 0.52
## [308,] 6.0 14.0 0.99940 3.19 0.62
## [309,] 5.0 12.0 0.99940 3.19 0.63
## [310,] 9.0 30.0 0.99680 3.41 0.53
## [311,] 6.0 25.0 0.99980 3.12 0.59
## [312,] 15.0 105.0 0.99600 3.27 0.54
## [313,] 19.0 98.0 0.99815 3.32 0.63
## [314,] 30.0 135.0 0.99760 3.30 0.53
## [315,] 26.0 72.0 0.99645 3.39 0.68
## [316,] 20.0 53.0 0.99620 3.42 0.65
## [317,] 37.0 92.0 0.99960 3.30 0.65
## [318,] 30.0 74.0 0.99865 3.30 0.64
## [319,] 21.0 59.0 0.99890 3.37 0.71
## [320,] 30.0 74.0 0.99865 3.30 0.64
## [321,] 21.0 59.0 0.99890 3.37 0.71
## [322,] 25.0 87.0 0.99975 3.24 0.62
## [323,] 6.0 18.0 0.99735 3.29 0.63
## [324,] 35.0 73.0 0.99900 3.28 0.70
## [325,] 13.0 50.0 1.00150 3.16 0.69
## [326,] 13.0 50.0 1.00150 3.16 0.69
## [327,] 6.0 14.0 0.99780 3.05 0.74
## [328,] 5.0 13.0 0.99800 3.28 0.83
## [329,] 6.0 21.0 1.00020 3.16 0.67
## [330,] 7.0 15.0 0.99810 3.18 0.62
## [331,] 10.0 41.0 0.99800 3.23 0.66
## [332,] 10.0 41.0 0.99800 3.23 0.66
## [333,] 21.0 114.0 0.99730 3.22 0.54
## [334,] 16.0 44.0 0.99580 3.13 0.52
## [335,] 17.0 38.0 0.99630 3.34 0.74
## [336,] 7.0 21.0 0.99920 3.17 0.84
## [337,] 6.0 16.0 0.99480 3.35 0.70
## [338,] 29.0 58.0 0.99740 3.31 0.64
## [339,] 28.0 99.0 1.00080 3.16 1.00
## [340,] 12.0 29.0 0.99970 3.11 1.36
## [341,] 10.0 21.0 1.00000 3.12 1.18
## [342,] 5.0 18.0 0.99720 3.21 0.87
## [343,] 6.0 14.0 0.99820 3.30 0.75
## [344,] 6.0 14.0 0.99820 3.30 0.75
## [345,] 6.0 32.0 1.00060 3.12 0.78
## [346,] 40.0 63.0 0.99790 3.60 0.81
## [347,] 17.0 34.0 0.99550 3.58 0.89
## [348,] 6.0 15.0 0.99860 3.02 0.93
## [349,] 15.0 46.0 0.99790 3.11 0.92
## [350,] 11.0 28.0 0.99940 3.36 0.86
## [351,] 17.0 34.0 1.00040 3.28 0.98
## [352,] 11.0 26.0 0.99940 3.35 0.83
## [353,] 8.0 19.0 0.99740 3.27 0.73
## [354,] 23.0 77.0 1.00180 3.18 0.77
## [355,] 40.5 165.0 0.99120 3.25 0.59
## [356,] 17.0 32.0 0.99550 3.55 0.61
## [357,] 29.0 55.0 1.00010 3.26 0.88
## [358,] 12.0 29.0 0.99700 3.24 0.75
## [359,] 10.0 23.0 1.00000 3.15 0.85
## [360,] 10.0 41.0 1.00100 3.17 0.68
## [361,] 14.0 81.0 0.99730 3.19 0.70
## [362,] 21.0 50.0 0.99820 3.37 0.91
## [363,] 6.0 37.0 0.99920 3.05 0.56
## [364,] 6.0 15.0 0.99880 2.99 0.87
## [365,] 7.0 42.0 1.00220 3.07 0.73
## [366,] 7.0 21.0 0.99790 3.26 0.93
## [367,] 7.0 42.0 1.00220 3.07 0.73
## [368,] 11.0 24.0 1.00000 3.16 0.69
## [369,] 15.0 75.0 0.99980 3.18 0.64
## [370,] 6.0 18.0 0.99620 3.20 1.13
## [371,] 35.0 63.0 0.99750 3.57 0.78
## [372,] 11.0 25.0 0.99670 3.32 0.87
## [373,] 26.0 46.0 0.99670 3.32 1.04
## [374,] 12.0 72.0 0.99590 3.05 0.63
## [375,] 6.0 47.0 1.00140 3.01 0.81
## [376,] 6.0 15.0 0.99840 3.01 0.83
## [377,] 19.0 47.0 1.00030 3.26 1.11
## [378,] 6.0 18.0 0.99620 3.20 1.13
## [379,] 6.0 24.0 0.99880 3.11 0.99
## [380,] 24.0 60.0 0.99790 3.31 0.70
## [381,] 11.0 27.0 0.99740 3.21 0.80
## [382,] 17.0 43.0 1.00140 3.06 0.80
## [383,] 11.0 27.0 0.99740 3.21 0.80
## [384,] 11.0 27.0 0.99740 3.21 0.80
## [385,] 23.0 54.0 0.99800 3.42 0.74
## [386,] 11.0 37.0 0.99790 3.43 0.76
## [387,] 23.0 48.0 0.99810 3.41 0.74
## [388,] 17.0 42.0 0.99720 3.31 0.54
## [389,] 23.0 53.0 0.99810 3.43 0.74
## [390,] 16.0 49.0 0.99820 3.19 0.70
## [391,] 12.0 88.0 0.99240 3.56 0.82
## [392,] 17.0 43.0 1.00140 3.06 0.80
## [393,] 6.0 26.0 0.99800 3.18 0.51
## [394,] 13.0 95.0 0.99700 3.16 0.54
## [395,] 6.0 25.0 0.99970 3.03 0.57
## [396,] 7.0 38.0 1.00040 3.22 0.60
## [397,] 68.0 124.0 0.99940 3.47 0.53
## [398,] 13.0 49.0 0.99880 3.18 0.65
## [399,] 13.0 49.0 0.99880 3.18 0.65
## [400,] 9.0 42.0 0.99630 3.10 0.55
## [401,] 68.0 124.0 0.99940 3.47 0.53
## [402,] 20.0 38.0 0.99490 3.29 0.47
## [403,] 19.0 64.0 1.00000 3.10 0.61
## [404,] 10.0 41.0 0.99940 3.15 0.63
## [405,] 15.0 27.0 0.99740 3.26 0.61
## [406,] 11.0 25.0 0.99660 3.36 0.76
## [407,] 9.0 28.0 0.99810 3.24 0.65
## [408,] 12.0 30.0 0.99960 3.18 0.63
## [409,] 6.0 16.0 0.99700 3.19 0.70
## [410,] 26.0 49.0 0.99810 3.05 0.57
## [411,] 26.0 86.0 0.99870 3.38 0.62
## [412,] 23.0 78.0 0.99870 3.38 0.62
## [413,] 15.0 77.0 0.99660 3.27 0.64
## [414,] 6.0 19.0 0.99860 3.27 0.82
## [415,] 24.0 122.0 0.99820 3.26 0.61
## [416,] 31.0 134.0 1.00140 3.32 1.07
## [417,] 6.0 20.0 0.99700 3.26 0.66
## [418,] 34.0 124.0 0.99560 3.44 0.48
## [419,] 7.0 20.0 0.99960 3.24 0.76
## [420,] 34.0 52.0 0.99760 3.62 0.68
## [421,] 35.0 67.0 0.99720 3.28 0.73
## [422,] 32.0 48.0 0.99520 3.52 0.56
## [423,] 15.0 60.0 0.99550 3.36 0.44
## [424,] 6.0 24.0 0.99780 3.15 0.90
## [425,] 15.0 60.0 0.99550 3.36 0.44
## [426,] 32.0 48.0 0.99520 3.52 0.56
## [427,] 19.0 48.0 0.99490 3.49 0.49
## [428,] 6.0 32.0 0.99880 3.26 0.56
## [429,] 9.0 30.0 0.99780 3.24 0.60
## [430,] 13.0 35.0 0.99970 3.10 0.60
## [431,] 6.0 24.0 0.99780 3.15 0.90
## [432,] 21.0 66.0 0.99740 3.25 0.56
## [433,] 12.0 24.0 0.99580 3.00 0.65
## [434,] 6.0 18.0 1.00040 3.16 0.49
## [435,] 22.0 54.0 0.99960 3.15 0.89
## [436,] 6.0 18.0 1.00040 3.16 0.49
## [437,] 38.0 62.0 0.99580 3.26 0.56
## [438,] 6.0 22.0 0.99860 3.17 0.66
## [439,] 22.0 54.0 0.99960 3.15 0.89
## [440,] 7.0 50.0 0.99510 3.08 0.60
## [441,] 6.0 29.0 0.99870 2.88 0.82
## [442,] 7.0 27.0 0.99880 3.06 0.68
## [443,] 6.0 43.0 1.00320 2.95 0.68
## [444,] 11.0 19.0 0.99630 3.23 0.63
## [445,] 5.0 27.0 0.99340 3.57 0.84
## [446,] 6.0 31.0 0.99860 3.23 0.56
## [447,] 31.0 72.0 0.99960 3.10 0.73
## [448,] 6.0 16.0 0.99680 3.22 0.72
## [449,] 7.0 27.0 0.99670 3.20 0.56
## [450,] 17.0 35.0 0.99940 3.10 0.61
## [451,] 17.0 35.0 0.99940 3.10 0.61
## [452,] 9.0 26.0 0.99790 3.06 1.06
## [453,] 18.0 35.0 0.99680 3.44 0.63
## [454,] 5.0 22.0 0.99980 3.26 0.74
## [455,] 21.0 67.0 0.99520 3.50 0.63
## [456,] 6.0 19.0 0.99880 3.22 0.69
## [457,] 5.0 22.0 0.99860 3.37 0.58
## [458,] 29.0 65.0 0.99880 3.28 0.58
## [459,] 5.0 22.0 0.99980 3.26 0.74
## [460,] 10.0 47.0 1.00080 3.25 0.57
## [461,] 14.0 23.0 0.99760 3.35 0.61
## [462,] 6.0 19.0 0.99820 3.26 0.61
## [463,] 10.0 25.0 0.99700 3.18 0.61
## [464,] 25.0 129.0 0.99720 3.08 0.53
## [465,] 5.0 15.0 0.99870 2.98 0.70
## [466,] 10.0 26.0 1.00060 3.48 0.91
## [467,] 21.0 51.0 0.99820 3.16 0.72
## [468,] 7.0 18.0 0.99470 3.32 0.79
## [469,] 6.0 21.0 1.00000 3.17 0.62
## [470,] 36.0 48.0 0.99520 3.20 0.58
## [471,] 15.0 47.0 0.99960 3.05 0.61
## [472,] 25.0 52.0 0.99700 3.20 0.71
## [473,] 25.0 68.0 0.99950 3.15 0.82
## [474,] 5.0 14.0 0.99710 3.26 0.79
## [475,] 10.0 24.0 0.99820 3.20 0.89
## [476,] 5.0 28.0 0.99880 3.14 0.60
## [477,] 6.0 16.0 0.99800 3.28 0.70
## [478,] 6.0 20.0 0.99770 3.18 1.06
## [479,] 5.0 28.0 0.99880 3.14 0.60
## [480,] 6.0 31.0 0.99840 3.19 0.70
## [481,] 6.0 23.0 1.00260 3.12 0.66
## [482,] 6.0 17.0 0.99640 3.15 0.92
## [483,] 6.0 18.0 0.99860 3.04 1.05
## [484,] 7.0 18.0 0.99860 3.04 1.06
## [485,] 6.0 24.0 0.99700 3.06 0.66
## [486,] 6.0 17.0 0.99760 3.17 0.47
## [487,] 6.0 17.0 0.99760 3.17 0.47
## [488,] 5.0 14.0 0.99820 3.17 0.42
## [489,] 35.0 67.0 1.00020 3.32 0.92
## [490,] 10.0 26.0 0.99840 3.34 0.75
## [491,] 24.0 56.0 0.99840 3.31 0.67
## [492,] 12.0 25.0 0.99520 3.34 0.79
## [493,] 13.0 27.0 0.99500 3.32 0.90
## [494,] 23.0 81.0 1.00020 3.48 0.74
## [495,] 28.0 91.0 0.99520 3.44 0.55
## [496,] 5.0 16.0 0.99720 3.15 0.65
## [497,] 14.0 38.0 0.99840 3.43 0.65
## [498,] 43.0 113.0 0.99660 3.32 0.79
## [499,] 5.0 16.0 0.99720 3.15 0.65
## [500,] 23.0 81.0 1.00020 3.48 0.74
## [501,] 14.0 38.0 0.99840 3.43 0.65
## [502,] 38.0 76.0 0.99900 3.17 0.85
## [503,] 38.0 76.0 0.99900 3.17 0.85
## [504,] 6.0 24.0 0.99760 3.18 1.04
## [505,] 6.0 22.0 0.99760 3.21 1.05
## [506,] 10.0 26.0 0.99680 3.16 0.78
## [507,] 6.0 21.0 0.99760 3.25 1.02
## [508,] 6.0 13.0 1.00000 3.17 0.71
## [509,] 26.0 62.0 0.99940 3.18 0.63
## [510,] 23.0 43.0 0.99860 3.04 0.68
## [511,] 19.0 43.0 0.99780 3.02 0.61
## [512,] 26.0 62.0 0.99940 3.18 0.63
## [513,] 15.0 49.0 0.99800 3.03 0.81
## [514,] 6.0 15.0 0.99730 3.09 0.66
## [515,] 6.0 15.0 0.99730 3.09 0.66
## [516,] 34.0 151.0 1.00100 3.31 1.14
## [517,] 5.0 14.0 1.00100 3.25 0.74
## [518,] 5.0 16.0 0.99940 3.16 0.63
## [519,] 11.0 32.0 0.99720 3.22 0.68
## [520,] 39.0 106.0 0.99660 3.36 0.78
## [521,] 15.0 33.0 0.99820 3.42 0.90
## [522,] 16.0 43.0 0.99800 3.48 0.64
## [523,] 47.0 133.0 0.99790 3.38 0.99
## [524,] 38.0 142.0 0.99780 3.22 0.55
## [525,] 23.0 116.0 0.99760 3.23 0.64
## [526,] 29.0 53.0 0.99980 3.24 0.67
## [527,] 39.0 106.0 0.99660 3.36 0.78
## [528,] 33.0 85.0 0.99620 3.39 0.77
## [529,] 32.0 55.0 0.99880 3.34 0.75
## [530,] 6.0 33.0 0.99740 3.09 0.57
## [531,] 1.0 28.0 0.99900 3.41 0.87
## [532,] 12.0 42.0 1.00040 3.16 0.61
## [533,] 12.0 42.0 1.00040 3.16 0.61
## [534,] 15.0 33.0 0.99560 3.22 0.66
## [535,] 13.0 32.0 1.00000 3.28 0.56
## [536,] 1.0 28.0 0.99900 3.41 0.87
## [537,] 6.0 33.0 0.99740 3.09 0.57
## [538,] 5.0 13.0 0.99720 3.37 0.77
## [539,] 5.0 35.0 1.00140 3.20 0.66
## [540,] 5.0 17.0 0.99960 3.22 0.62
## [541,] 20.0 47.0 0.99880 3.26 0.67
## [542,] 5.0 17.0 0.99880 3.21 0.73
## [543,] 5.0 20.0 0.99660 3.12 0.59
## [544,] 16.0 53.0 0.99930 3.06 0.72
## [545,] 6.0 15.0 1.00080 2.86 0.79
## [546,] 38.0 106.0 0.99820 3.08 0.59
## [547,] 10.0 28.0 0.99830 3.45 0.78
## [548,] 6.0 21.0 0.99870 3.26 0.86
## [549,] 27.0 69.0 0.99940 3.12 0.75
## [550,] 6.0 25.0 0.99750 3.27 0.61
## [551,] 9.0 25.0 0.99580 3.33 0.56
## [552,] 14.0 45.0 0.99800 3.19 0.73
## [553,] 14.0 44.0 0.99800 3.12 0.74
## [554,] 32.0 96.0 0.99340 3.74 0.62
## [555,] 10.0 23.0 1.00315 2.92 0.74
## [556,] 10.0 23.0 1.00315 2.92 0.74
## [557,] 10.0 17.0 1.00020 3.07 0.56
## [558,] 10.0 23.0 1.00315 2.92 0.74
## [559,] 10.0 17.0 1.00020 3.07 0.56
## [560,] 6.0 47.0 1.00210 3.30 0.68
## [561,] 5.0 19.0 0.99940 3.14 0.57
## [562,] 26.0 121.0 0.99780 3.23 0.58
## [563,] 41.0 110.0 0.99820 3.08 0.61
## [564,] 25.0 60.0 0.99710 3.31 0.61
## [565,] 6.0 47.0 1.00210 3.30 0.68
## [566,] 5.0 19.0 0.99940 3.14 0.57
## [567,] 5.0 15.0 0.99910 3.32 0.60
## [568,] 5.0 15.0 0.99910 3.32 0.60
## [569,] 5.0 20.0 0.99900 3.31 0.79
## [570,] 19.0 42.0 0.99460 3.57 0.57
## [571,] 10.0 37.0 1.00030 3.32 0.91
## [572,] 19.0 42.0 0.99460 3.57 0.57
## [573,] 10.0 28.0 0.99780 3.14 0.61
## [574,] 14.0 47.0 0.99910 3.30 0.56
## [575,] 20.0 78.0 0.99920 3.19 0.70
## [576,] 28.0 46.0 0.99930 3.20 0.80
## [577,] 6.0 14.0 0.99780 3.34 0.52
## [578,] 18.0 111.0 0.99820 3.30 0.60
## [579,] 17.0 110.0 0.99820 3.29 0.60
## [580,] 18.0 40.0 0.99760 3.14 0.51
## [581,] 5.0 14.0 1.00020 3.19 0.44
## [582,] 5.0 14.0 1.00020 3.19 0.44
## [583,] 5.0 15.0 1.00000 3.19 0.43
## [584,] 10.0 21.0 0.99760 2.98 0.66
## [585,] 54.0 80.0 1.00020 3.30 0.76
## [586,] 8.0 38.0 0.99800 3.47 0.66
## [587,] 16.0 47.0 0.99860 3.12 1.02
## [588,] 18.0 102.0 0.99670 3.26 0.59
## [589,] 19.0 50.0 0.99170 3.72 0.74
## [590,] 5.0 13.0 0.99760 3.05 0.74
## [591,] 21.0 75.0 0.99870 3.35 0.57
## [592,] 23.0 149.0 0.99220 3.12 0.50
## [593,] 21.0 75.0 0.99870 3.35 0.57
## [594,] 9.0 43.0 1.00040 3.29 0.58
## [595,] 20.0 38.0 0.99780 3.40 0.53
## [596,] 30.0 109.0 0.99980 3.27 0.57
## [597,] 6.0 24.0 0.99940 3.04 0.60
## [598,] 5.0 18.0 0.99800 3.09 0.63
## [599,] 5.0 30.0 0.99670 3.20 0.48
## [600,] 11.0 22.0 1.00000 3.00 0.70
## [601,] 7.0 23.0 0.99620 3.26 0.47
## [602,] 12.0 35.0 1.00060 3.10 0.56
## [603,] 6.0 14.0 0.99750 3.30 0.52
## [604,] 12.0 35.0 1.00060 3.10 0.56
## [605,] 14.0 63.0 0.99840 3.17 0.62
## [606,] 6.0 24.0 0.99840 3.31 0.59
## [607,] 10.0 20.0 0.99730 3.34 0.79
## [608,] 26.0 52.0 0.99820 3.31 0.53
## [609,] 11.0 65.0 1.00260 3.32 0.64
## [610,] 15.0 39.0 0.99560 3.56 0.52
## [611,] 25.0 57.0 0.99830 3.39 0.54
## [612,] 5.0 16.0 1.00060 2.98 0.54
## [613,] 18.0 29.0 0.99650 3.32 0.60
## [614,] 10.0 29.0 0.99620 3.26 0.74
## [615,] 10.0 103.0 0.99690 2.87 1.36
## [616,] 28.0 71.0 0.99970 3.50 0.57
## [617,] 28.0 71.0 0.99970 3.50 0.57
## [618,] 14.0 28.0 0.99820 3.03 0.93
## [619,] 4.0 17.0 1.00060 3.13 0.70
## [620,] 6.0 16.0 0.99880 3.09 0.80
## [621,] 16.0 112.0 0.99760 3.27 0.61
## [622,] 14.0 104.0 0.99760 3.28 0.62
## [623,] 9.0 32.0 0.99740 3.13 0.55
## [624,] 21.0 45.0 0.99740 3.49 0.96
## [625,] 21.0 58.0 0.99970 3.46 0.72
## [626,] 21.0 58.0 0.99970 3.46 0.72
## [627,] 5.0 15.0 0.99880 3.36 0.49
## [628,] 5.0 15.0 0.99880 3.36 0.49
## [629,] 7.0 31.0 0.99760 3.27 0.60
## [630,] 20.0 84.0 0.99640 3.21 0.61
## [631,] 7.0 31.0 0.99760 3.27 0.60
## [632,] 5.0 19.0 0.99880 3.25 0.63
## [633,] 21.0 43.0 0.99640 3.32 0.57
## [634,] 11.0 86.0 1.00100 3.43 0.64
## [635,] 46.0 102.0 0.99640 3.27 0.58
## [636,] 24.0 33.0 0.99540 3.27 0.55
## [637,] 30.0 147.0 0.99790 3.24 0.53
## [638,] 31.0 145.0 0.99780 3.24 0.53
## [639,] 7.0 23.0 0.99640 3.35 0.65
## [640,] 25.0 57.0 0.99700 3.18 1.36
## [641,] 16.0 40.0 0.99910 3.39 0.62
## [642,] 21.0 68.0 0.99920 3.46 0.63
## [643,] 16.0 40.0 0.99910 3.39 0.62
## [644,] 21.0 68.0 0.99920 3.46 0.63
## [645,] 16.0 40.0 0.99910 3.39 0.62
## [646,] 5.0 11.0 0.99840 3.37 0.69
## [647,] 6.0 20.0 0.99720 3.40 0.63
## [648,] 5.0 14.0 0.99670 3.32 0.58
## [649,] 10.0 28.0 0.99640 3.33 0.67
## [650,] 24.0 148.0 0.99480 3.16 0.57
## [651,] 8.0 32.0 0.99860 2.89 0.50
## [652,] 35.0 155.0 1.00100 3.41 0.67
## [653,] 22.0 71.0 0.99760 2.98 0.84
## [654,] 9.0 30.0 0.99760 3.12 0.54
## [655,] 7.0 29.0 0.99790 3.08 0.46
## [656,] 20.0 53.0 1.00040 3.14 0.61
## [657,] 8.0 32.0 0.99860 2.89 0.50
## [658,] 23.0 42.0 0.99800 2.92 0.68
## [659,] 5.0 20.0 0.99730 3.32 0.81
## [660,] 5.0 13.0 0.99700 3.41 0.57
## [661,] 5.0 20.0 0.99730 3.32 0.81
## [662,] 15.0 42.0 0.99780 3.31 0.64
## [663,] 9.0 27.0 0.99720 3.36 0.70
## [664,] 5.0 13.0 0.99740 3.04 0.79
## [665,] 15.0 54.0 1.00000 3.03 0.66
## [666,] 25.0 48.0 0.99810 3.14 0.56
## [667,] 6.0 16.0 0.99800 3.18 0.60
## [668,] 6.0 15.0 0.99880 2.94 0.66
## [669,] 15.0 31.0 0.99660 3.15 0.57
## [670,] 6.0 15.0 0.99880 2.94 0.66
## [671,] 30.0 45.0 0.99590 3.26 0.58
## [672,] 5.0 13.0 0.99680 3.20 0.52
## [673,] 32.0 151.0 0.99800 3.15 0.53
## [674,] 5.0 13.0 0.99680 3.20 0.52
## [675,] 7.0 17.0 0.99840 3.08 0.67
## [676,] 12.0 31.0 0.99840 3.26 0.65
## [677,] 7.0 17.0 0.99840 3.08 0.67
## [678,] 12.0 31.0 0.99790 3.40 0.48
## [679,] 45.0 87.0 0.99830 3.48 0.53
## [680,] 20.0 49.0 0.99720 3.13 0.54
## [681,] 15.0 40.0 1.00040 3.06 0.49
## [682,] 16.0 29.0 0.99620 3.21 0.49
## [683,] 32.0 58.0 0.99800 3.33 0.54
## [684,] 5.0 15.0 0.99700 3.37 0.56
## [685,] 35.0 152.0 0.99800 3.25 0.48
## [686,] 5.0 15.0 0.99700 3.37 0.56
## [687,] 13.0 40.0 0.99700 3.44 0.60
## [688,] 13.0 38.0 0.99980 3.28 0.59
## [689,] 4.0 9.0 0.99620 3.40 0.47
## [690,] 5.0 17.0 0.99760 3.30 1.05
## [691,] 5.0 14.0 0.99660 3.63 0.54
## [692,] 12.0 93.0 0.99980 3.48 0.54
## [693,] 16.0 62.0 0.99790 3.03 1.17
## [694,] 21.0 122.0 0.99840 3.32 0.62
## [695,] 24.0 125.0 0.99840 3.31 0.61
## [696,] 18.0 44.0 0.99210 3.90 0.62
## [697,] 8.0 25.0 0.99720 3.47 0.67
## [698,] 8.0 25.0 0.99720 3.47 0.67
## [699,] 18.0 72.0 1.00010 3.31 0.53
## [700,] 5.0 19.0 0.99860 3.11 0.62
## [701,] 26.0 88.0 0.99840 3.08 0.57
## [702,] 8.0 25.0 0.99720 3.47 0.67
## [703,] 9.0 23.0 0.99700 3.47 0.67
## [704,] 22.0 84.0 0.99720 3.32 0.70
## [705,] 4.0 14.0 0.99800 3.29 0.54
## [706,] 11.0 54.0 0.99900 3.37 0.49
## [707,] 10.0 19.0 0.99560 3.40 0.47
## [708,] 16.0 37.0 0.99660 3.37 0.51
## [709,] 11.0 35.0 0.99600 3.34 0.61
## [710,] 13.0 33.0 0.99830 3.27 0.66
## [711,] 21.0 84.0 0.99850 3.06 0.57
## [712,] 16.0 112.0 0.99980 3.38 0.61
## [713,] 13.0 33.0 0.99920 3.36 0.45
## [714,] 10.0 48.0 0.99760 3.34 0.46
## [715,] 21.0 77.0 0.99800 3.28 0.51
## [716,] 13.0 34.0 0.99670 3.29 0.48
## [717,] 10.0 48.0 0.99760 3.34 0.46
## [718,] 12.0 49.0 0.99680 3.21 0.57
## [719,] 10.0 22.0 0.99760 3.22 0.44
## [720,] 17.0 45.0 0.99760 3.46 0.54
## [721,] 10.0 22.0 0.99760 3.22 0.44
## [722,] 35.0 106.0 0.99820 3.10 0.53
## [723,] 15.0 48.0 0.99680 3.21 0.59
## [724,] 36.0 127.0 0.99650 2.94 1.62
## [725,] 5.0 12.0 0.99580 3.54 0.60
## [726,] 5.0 13.0 0.99760 3.29 0.55
## [727,] 18.0 49.0 0.99940 3.43 0.72
## [728,] 4.0 11.0 0.99700 3.46 0.68
## [729,] 4.0 11.0 0.99700 3.46 0.68
## [730,] 27.0 58.0 0.99500 3.61 0.49
## [731,] 12.0 37.0 0.99820 3.17 0.67
## [732,] 4.0 14.0 0.99940 3.44 0.52
## [733,] 10.0 19.0 0.99660 3.39 0.47
## [734,] 16.0 72.0 0.99800 3.55 0.60
## [735,] 9.0 39.0 0.99870 3.48 0.62
## [736,] 11.0 22.0 0.99630 3.26 0.50
## [737,] 11.0 22.0 0.99630 3.26 0.50
## [738,] 19.0 58.0 1.00020 3.50 0.65
## [739,] 28.0 104.0 0.99830 3.10 0.56
## [740,] 19.0 38.0 0.99900 3.35 0.60
## [741,] 12.0 16.0 0.99650 3.45 0.68
## [742,] 28.0 139.0 0.99788 3.21 0.57
## [743,] 9.0 18.0 0.99720 3.46 0.65
## [744,] 25.0 101.0 1.00024 3.13 0.53
## [745,] 21.0 101.0 1.00010 3.13 0.51
## [746,] 12.0 28.0 0.99768 3.52 0.73
## [747,] 12.0 57.0 0.99780 3.30 0.47
## [748,] 16.0 68.0 0.99782 3.30 0.48
## [749,] 12.0 31.0 0.99761 3.52 0.72
## [750,] 12.0 28.0 0.99768 3.52 0.73
## [751,] 17.0 40.0 0.99803 3.29 0.55
## [752,] 17.0 40.0 0.99803 3.29 0.55
## [753,] 24.0 66.0 0.99785 3.39 0.61
## [754,] 17.0 40.0 0.99803 3.29 0.55
## [755,] 14.0 32.0 0.99656 3.09 1.06
## [756,] 22.0 47.0 0.99525 3.51 0.43
## [757,] 15.0 33.0 0.99488 3.60 0.46
## [758,] 10.0 31.0 0.99656 3.25 0.50
## [759,] 10.0 31.0 0.99656 3.25 0.50
## [760,] 33.0 88.0 0.99823 3.19 0.52
## [761,] 9.0 104.0 0.99779 3.23 0.57
## [762,] 5.0 35.0 0.99738 3.25 0.42
## [763,] 8.0 19.0 0.99701 3.31 0.53
## [764,] 5.0 35.0 0.99738 3.25 0.42
## [765,] 11.0 44.0 0.99888 3.31 0.55
## [766,] 12.0 48.0 0.99888 3.31 0.54
## [767,] 12.0 74.0 0.99738 3.14 0.54
## [768,] 13.0 98.0 0.99938 3.45 0.62
## [769,] 24.0 94.0 0.99744 3.55 0.53
## [770,] 7.0 32.0 0.99668 3.39 0.54
## [771,] 24.0 94.0 0.99744 3.55 0.53
## [772,] 23.0 143.0 0.99780 3.28 0.55
## [773,] 23.0 144.0 0.99782 3.27 0.55
## [774,] 1.0 44.0 0.99730 3.30 0.92
## [775,] 2.0 45.0 0.99727 3.31 0.91
## [776,] 7.0 16.0 0.99586 3.43 0.46
## [777,] 5.5 48.0 0.99612 3.40 0.60
## [778,] 6.0 18.0 0.99610 3.40 0.59
## [779,] 7.0 34.0 0.99788 3.36 0.61
## [780,] 21.0 92.0 0.99745 3.50 0.60
## [781,] 12.0 45.0 0.99676 3.31 0.60
## [782,] 9.0 37.0 0.99732 3.66 0.65
## [783,] 26.0 96.0 0.99814 3.36 0.53
## [784,] 9.0 37.0 0.99732 3.66 0.65
## [785,] 20.0 85.0 0.99746 3.55 0.59
## [786,] 11.0 61.0 0.99820 3.21 0.50
## [787,] 11.0 61.0 0.99820 3.21 0.50
## [788,] 19.0 58.0 0.99910 3.18 0.56
## [789,] 19.0 58.0 0.99910 3.18 0.56
## [790,] 21.0 119.0 0.99800 3.09 0.52
## [791,] 18.0 82.0 0.99708 3.33 0.68
## [792,] 25.0 130.0 0.99818 3.23 0.54
## [793,] 17.0 87.0 0.99745 3.48 0.60
## [794,] 7.0 13.0 0.99639 3.38 0.56
## [795,] 7.0 26.0 0.99531 3.17 0.53
## [796,] 7.0 60.0 0.99786 2.99 1.18
## [797,] 24.0 64.0 0.99746 3.10 0.74
## [798,] 21.0 42.0 0.99526 3.24 0.81
## [799,] 5.0 14.0 0.99870 3.29 0.52
## [800,] 5.0 14.0 0.99870 3.29 0.52
## [801,] 26.0 108.0 0.99641 3.25 0.51
## [802,] 8.0 17.0 0.99735 3.23 0.44
## [803,] 14.0 86.0 0.99264 3.56 0.94
## [804,] 14.0 46.0 0.99710 3.24 0.66
## [805,] 4.0 18.0 0.99682 3.26 0.57
## [806,] 4.0 10.0 0.99356 3.33 0.70
## [807,] 4.0 10.0 0.99386 3.27 0.71
## [808,] 4.0 10.0 0.99356 3.33 0.70
## [809,] 4.0 12.0 0.99702 3.26 0.86
## [810,] 4.0 15.0 0.99693 3.22 0.55
## [811,] 4.0 14.0 0.99562 3.30 0.47
## [812,] 7.0 24.0 1.00012 3.09 0.68
## [813,] 20.0 38.0 0.99818 3.24 0.71
## [814,] 4.0 7.0 0.99462 3.44 0.58
## [815,] 19.0 41.0 0.99939 3.21 0.76
## [816,] 20.0 38.0 0.99818 3.24 0.71
## [817,] 8.0 30.0 0.99840 3.23 0.58
## [818,] 19.0 27.0 0.99545 3.28 0.73
## [819,] 21.0 47.0 0.99632 3.29 0.45
## [820,] 9.0 59.0 0.99976 3.28 0.54
## [821,] 9.0 22.0 0.99606 3.34 0.60
## [822,] 16.0 42.0 0.99154 3.71 0.74
## [823,] 15.0 36.0 0.99730 3.61 0.64
## [824,] 15.0 36.0 0.99730 3.61 0.64
## [825,] 6.0 16.0 0.99682 3.24 0.53
## [826,] 15.0 37.0 0.99624 3.36 0.49
## [827,] 4.0 8.0 0.99510 3.40 0.64
## [828,] 15.0 37.0 0.99624 3.36 0.49
## [829,] 34.0 45.0 0.99417 3.46 0.74
## [830,] 16.0 24.0 0.99376 3.56 0.77
## [831,] 5.0 9.0 0.99632 3.38 0.55
## [832,] 16.0 24.0 0.99376 3.56 0.77
## [833,] 34.0 48.0 0.99832 3.38 0.86
## [834,] 36.0 51.0 0.99836 3.38 0.86
## [835,] 16.0 23.0 0.99694 3.32 0.47
## [836,] 27.0 55.0 0.99655 3.39 0.51
## [837,] 36.0 100.0 0.99064 3.26 0.39
## [838,] 36.0 100.0 0.99064 3.26 0.39
## [839,] 9.0 28.0 0.99672 3.24 0.83
## [840,] 13.0 26.0 0.99647 3.46 0.47
## [841,] 9.0 34.0 0.99736 3.24 0.77
## [842,] 21.0 31.0 0.99629 3.45 0.63
## [843,] 34.0 68.0 0.99708 3.23 0.72
## [844,] 9.0 92.0 0.99630 3.28 0.62
## [845,] 26.0 42.0 0.99590 3.18 0.83
## [846,] 14.0 31.0 0.99689 3.59 0.66
## [847,] 14.0 31.0 0.99689 3.59 0.66
## [848,] 12.0 39.0 0.99770 3.50 0.70
## [849,] 14.0 31.0 0.99689 3.59 0.66
## [850,] 12.0 32.0 0.99689 3.58 0.66
## [851,] 9.0 22.0 0.99708 3.28 0.55
## [852,] 9.0 22.0 0.99708 3.28 0.55
## [853,] 26.0 122.0 0.99801 3.22 1.07
## [854,] 41.0 55.0 0.99652 3.47 0.73
## [855,] 41.0 55.0 0.99652 3.47 0.73
## [856,] 10.0 14.0 0.99538 3.51 0.71
## [857,] 41.0 55.0 0.99652 3.47 0.73
## [858,] 16.0 47.0 0.99594 3.40 0.78
## [859,] 17.0 32.0 0.99686 3.15 0.67
## [860,] 15.0 24.0 0.99438 3.40 0.82
## [861,] 15.0 85.0 0.99746 3.51 0.54
## [862,] 15.0 88.0 0.99357 3.66 0.60
## [863,] 7.0 25.0 0.99628 3.24 0.44
## [864,] 17.0 84.0 0.99746 3.51 0.53
## [865,] 15.0 85.0 0.99746 3.51 0.54
## [866,] 16.0 86.0 0.99748 3.51 0.54
## [867,] 13.0 24.0 0.99438 3.41 0.83
## [868,] 13.0 22.0 0.99438 3.40 0.84
## [869,] 15.0 24.0 0.99438 3.40 0.82
## [870,] 27.0 43.0 0.99578 3.44 0.64
## [871,] 31.0 43.0 0.99371 3.41 0.57
## [872,] 36.0 46.0 0.99522 3.53 0.57
## [873,] 28.0 48.0 0.99576 3.43 0.54
## [874,] 6.0 10.0 0.99552 3.23 0.58
## [875,] 6.0 10.0 0.99664 3.12 0.65
## [876,] 7.0 16.0 0.99614 3.31 0.79
## [877,] 7.0 14.0 0.99517 3.40 0.58
## [878,] 31.0 43.0 0.99371 3.41 0.57
## [879,] 30.0 69.0 0.99787 3.22 0.50
## [880,] 18.0 88.0 0.99745 3.53 0.55
## [881,] 10.0 21.0 0.99576 3.15 0.49
## [882,] 30.0 35.0 0.99533 3.48 0.65
## [883,] 14.0 26.0 0.99536 3.22 0.78
## [884,] 18.0 88.0 0.99745 3.53 0.55
## [885,] 30.0 69.0 0.99787 3.22 0.50
## [886,] 9.0 30.0 0.99824 3.34 0.64
## [887,] 8.0 28.0 0.99836 3.33 0.65
## [888,] 29.0 56.0 0.99577 3.15 0.79
## [889,] 32.0 64.0 0.99491 3.43 0.56
## [890,] 23.0 99.0 1.00289 3.22 0.68
## [891,] 30.0 52.0 0.99576 3.44 0.58
## [892,] 16.0 86.0 0.99743 3.53 0.57
## [893,] 10.0 18.0 0.99774 3.22 0.65
## [894,] 16.0 86.0 0.99743 3.53 0.57
## [895,] 17.0 88.0 0.99745 3.53 0.58
## [896,] 27.0 43.0 0.99550 3.42 0.58
## [897,] 17.0 43.0 0.99444 3.31 0.77
## [898,] 27.0 43.0 0.99550 3.42 0.58
## [899,] 17.0 43.0 0.99444 3.31 0.77
## [900,] 6.0 11.0 0.99892 3.48 0.49
## [901,] 10.0 39.0 0.99562 3.40 0.69
## [902,] 16.0 33.0 0.99736 3.58 0.69
## [903,] 16.0 33.0 0.99736 3.58 0.69
## [904,] 11.0 18.0 0.99620 3.41 0.59
## [905,] 11.0 18.0 0.99620 3.41 0.59
## [906,] 15.0 115.0 0.99800 3.23 0.59
## [907,] 12.0 78.0 0.99640 3.39 0.71
## [908,] 6.0 9.0 0.99480 3.59 0.54
## [909,] 34.0 61.0 0.99528 3.43 0.59
## [910,] 7.0 11.0 0.99331 3.29 0.52
## [911,] 3.0 11.0 0.99577 3.23 0.57
## [912,] 3.0 9.0 0.99901 3.18 0.60
## [913,] 4.0 8.0 0.99674 3.33 0.62
## [914,] 3.0 10.0 0.99639 3.27 0.64
## [915,] 7.0 11.0 0.99331 3.29 0.52
## [916,] 3.0 6.0 0.99512 3.27 0.67
## [917,] 7.0 62.0 0.99395 3.62 0.61
## [918,] 26.0 45.0 0.99824 3.38 0.64
## [919,] 32.0 79.0 0.99640 3.30 0.72
## [920,] 38.0 46.0 0.99504 3.38 0.89
## [921,] 10.0 23.0 0.99786 3.24 0.56
## [922,] 32.0 79.0 0.99640 3.30 0.72
## [923,] 38.0 46.0 0.99504 3.38 0.89
## [924,] 26.0 45.0 0.99824 3.38 0.64
## [925,] 14.0 28.0 0.99516 3.18 0.80
## [926,] 53.0 77.0 0.99604 3.47 0.87
## [927,] 52.0 73.0 0.99786 3.47 0.90
## [928,] 11.0 75.0 0.99736 3.20 0.59
## [929,] 14.0 28.0 0.99516 3.18 0.80
## [930,] 10.0 19.0 0.99468 3.30 0.73
## [931,] 8.0 25.0 0.99746 3.69 0.73
## [932,] 13.0 38.0 0.99748 3.48 0.65
## [933,] 29.0 66.0 0.99710 3.45 0.59
## [934,] 13.0 38.0 0.99748 3.48 0.65
## [935,] 8.0 25.0 0.99746 3.69 0.73
## [936,] 19.0 72.0 0.99543 3.39 0.72
## [937,] 19.0 72.0 0.99543 3.39 0.72
## [938,] 6.0 26.0 0.99791 3.07 0.60
## [939,] 23.0 42.0 0.99356 3.34 0.72
## [940,] 7.0 11.0 0.99425 3.61 0.54
## [941,] 13.0 25.0 0.99509 3.36 0.76
## [942,] 9.0 17.0 0.99484 3.19 0.52
## [943,] 13.0 52.0 0.99834 3.22 0.64
## [944,] 10.0 45.0 0.99864 3.24 0.60
## [945,] 11.0 24.0 0.99498 3.27 0.64
## [946,] 7.0 20.0 0.99566 3.14 0.79
## [947,] 11.0 24.0 0.99745 3.29 0.99
## [948,] 6.0 12.0 0.99408 3.26 0.62
## [949,] 10.0 21.0 0.99552 3.41 0.76
## [950,] 10.0 21.0 0.99552 3.41 0.76
## [951,] 10.0 21.0 0.99552 3.41 0.76
## [952,] 6.0 12.0 0.99408 3.26 0.62
## [953,] 6.0 10.0 0.99536 3.31 0.68
## [954,] 5.0 9.0 0.99458 3.20 0.69
## [955,] 6.0 11.0 0.99538 3.44 0.54
## [956,] 9.0 23.0 0.99648 3.36 0.67
## [957,] 5.0 10.0 0.99568 3.31 0.62
## [958,] 12.0 51.0 0.99613 3.29 0.58
## [959,] 25.0 36.0 0.99519 3.47 0.71
## [960,] 12.0 32.0 0.99735 3.36 0.61
## [961,] 18.0 38.0 0.99518 3.16 0.85
## [962,] 7.0 18.0 0.99592 3.27 0.62
## [963,] 6.0 16.0 0.99654 3.38 0.69
## [964,] 20.0 27.0 0.99546 3.15 0.69
## [965,] 18.0 38.0 0.99518 3.16 0.85
## [966,] 24.0 48.0 0.99554 3.34 0.86
## [967,] 6.0 10.0 0.99604 3.13 0.58
## [968,] 23.0 113.0 0.99733 3.13 0.48
## [969,] 29.0 46.0 0.99430 3.20 0.60
## [970,] 7.0 22.0 0.99669 3.46 0.61
## [971,] 6.0 10.0 0.99724 3.33 0.87
## [972,] 6.0 10.0 0.99724 3.33 0.87
## [973,] 6.0 13.0 0.99643 3.22 0.65
## [974,] 6.0 12.0 0.99605 3.52 0.63
## [975,] 7.0 13.0 0.99658 3.30 0.62
## [976,] 35.0 72.0 0.99700 3.44 0.52
## [977,] 35.0 72.0 0.99700 3.44 0.52
## [978,] 31.0 119.0 0.99801 3.15 0.50
## [979,] 9.0 29.0 0.99416 3.28 0.49
## [980,] 3.0 6.0 0.99690 3.13 0.63
## [981,] 8.0 17.0 0.99774 3.32 0.71
## [982,] 13.0 28.0 0.99712 3.25 0.62
## [983,] 51.0 70.0 0.99418 3.34 0.82
## [984,] 8.0 17.0 0.99774 3.32 0.71
## [985,] 3.0 6.0 0.99690 3.13 0.63
## [986,] 7.0 11.0 0.99562 3.45 0.58
## [987,] 3.0 7.0 0.99470 3.19 0.55
## [988,] 35.0 70.0 0.99693 3.44 0.50
## [989,] 19.0 27.0 0.99596 3.16 0.49
## [990,] 14.0 21.0 0.99556 3.14 0.51
## [991,] 19.0 27.0 0.99596 3.16 0.49
## [992,] 31.0 68.0 0.99694 3.45 0.48
## [993,] 30.0 47.0 0.99554 3.36 0.48
## [994,] 31.0 68.0 0.99694 3.45 0.48
## [995,] 20.0 88.0 0.99918 3.15 0.43
## [996,] 19.0 41.0 0.99697 3.39 0.62
## [997,] 3.0 11.0 0.99378 3.71 0.63
## [998,] 3.0 11.0 0.99378 3.71 0.63
## [999,] 4.0 10.0 0.99554 3.12 0.48
## [1000,] 7.0 12.0 0.99162 3.47 0.53
## [1001,] 6.0 12.0 0.99495 3.44 0.72
## [1002,] 31.0 47.0 0.99676 3.26 0.82
## [1003,] 13.0 27.0 0.99516 3.26 0.84
## [1004,] 4.0 8.0 0.99280 3.36 0.55
## [1005,] 27.0 45.0 0.99603 3.25 0.54
## [1006,] 4.0 8.0 0.99280 3.36 0.55
## [1007,] 13.0 27.0 0.99516 3.26 0.84
## [1008,] 12.0 25.0 0.99516 3.26 0.84
## [1009,] 12.0 24.0 0.99549 3.23 0.70
## [1010,] 26.0 55.0 0.99722 3.18 0.68
## [1011,] 7.0 12.0 0.99354 3.25 0.55
## [1012,] 12.0 19.0 0.99570 3.17 0.55
## [1013,] 11.0 32.0 0.99604 3.23 0.48
## [1014,] 22.0 31.0 0.99635 3.38 0.58
## [1015,] 3.0 7.0 0.99454 3.22 0.58
## [1016,] 14.0 27.0 0.99598 3.06 0.84
## [1017,] 12.0 28.0 0.99486 3.27 0.75
## [1018,] 36.0 109.0 0.99007 2.89 0.44
## [1019,] 36.0 109.0 0.99007 2.89 0.44
## [1020,] 10.0 23.0 0.99636 3.53 0.61
## [1021,] 3.0 8.0 0.99642 3.20 0.53
## [1022,] 3.0 8.0 0.99642 3.20 0.53
## [1023,] 4.0 9.0 0.99584 3.35 0.54
## [1024,] 3.0 9.0 0.99506 3.27 0.55
## [1025,] 12.0 18.0 0.99568 3.32 0.56
## [1026,] 17.0 43.0 0.99822 3.33 0.60
## [1027,] 14.0 36.0 0.99364 3.41 0.56
## [1028,] 28.0 52.0 0.99378 3.49 0.52
## [1029,] 41.0 68.0 0.99586 3.37 0.54
## [1030,] 12.0 18.0 0.99568 3.32 0.56
## [1031,] 9.0 14.0 0.99488 3.42 0.55
## [1032,] 9.0 15.0 0.99514 3.35 0.58
## [1033,] 5.0 14.0 0.99854 3.36 0.53
## [1034,] 14.0 27.0 0.99592 3.30 0.59
## [1035,] 15.0 48.0 0.99739 3.20 0.47
## [1036,] 5.0 17.0 0.99683 3.17 0.65
## [1037,] 26.0 35.0 0.99356 3.22 0.59
## [1038,] 20.0 56.0 0.99672 3.35 0.56
## [1039,] 25.0 42.0 0.99530 3.24 0.77
## [1040,] 21.0 39.0 0.99692 3.33 0.83
## [1041,] 16.0 32.0 0.99756 3.58 0.67
## [1042,] 13.0 26.0 0.99547 3.38 0.64
## [1043,] 21.0 39.0 0.99692 3.33 0.83
## [1044,] 18.0 39.0 0.99859 3.29 0.81
## [1045,] 12.0 53.0 0.99294 3.36 0.62
## [1046,] 32.0 38.0 0.99438 3.32 0.58
## [1047,] 33.0 45.0 0.99612 3.31 0.62
## [1048,] 27.0 51.0 0.99634 3.49 0.64
## [1049,] 3.0 14.0 0.99702 3.27 0.78
## [1050,] 6.0 17.0 0.99704 3.27 0.77
## [1051,] 27.0 51.0 0.99634 3.49 0.64
## [1052,] 16.0 45.0 0.99702 3.03 1.34
## [1053,] 19.0 25.0 0.99258 3.56 0.55
## [1054,] 9.0 20.0 0.99426 3.38 0.77
## [1055,] 5.0 77.0 0.99747 3.13 0.62
## [1056,] 5.0 77.0 0.99747 3.13 0.62
## [1057,] 3.0 10.0 0.99586 3.21 0.59
## [1058,] 28.0 90.0 0.99784 3.15 0.57
## [1059,] 30.0 52.0 0.99710 3.19 0.76
## [1060,] 3.0 10.0 0.99586 3.21 0.59
## [1061,] 3.0 8.0 0.99810 3.14 0.70
## [1062,] 7.0 16.0 0.99462 3.21 0.69
## [1063,] 6.0 15.0 0.99560 3.30 0.64
## [1064,] 6.0 17.0 0.99565 3.22 0.63
## [1065,] 5.0 10.0 0.99418 3.28 0.57
## [1066,] 6.0 20.0 0.99630 3.29 0.60
## [1067,] 13.0 26.0 0.99358 3.40 0.72
## [1068,] 3.0 10.0 0.99572 3.02 0.83
## [1069,] 3.0 10.0 0.99572 3.02 0.83
## [1070,] 28.0 52.0 0.99700 3.31 0.62
## [1071,] 19.0 37.0 0.99498 3.18 0.89
## [1072,] 30.0 92.0 0.99892 3.20 0.58
## [1073,] 33.0 75.0 0.99680 3.40 0.58
## [1074,] 16.0 37.0 0.99720 3.31 0.58
## [1075,] 30.0 92.0 0.99892 3.20 0.58
## [1076,] 45.0 67.0 0.99769 3.44 0.86
## [1077,] 3.0 8.0 0.99534 3.15 0.73
## [1078,] 3.0 8.0 0.99817 3.27 0.58
## [1079,] 3.0 8.0 0.99817 3.27 0.58
## [1080,] 37.5 278.0 0.99316 3.01 0.51
## [1081,] 3.0 8.0 0.99471 3.16 0.51
## [1082,] 37.5 289.0 0.99316 3.01 0.51
## [1083,] 31.0 70.0 0.99685 3.42 0.59
## [1084,] 32.0 59.0 0.99617 3.33 0.77
## [1085,] 31.0 70.0 0.99685 3.42 0.59
## [1086,] 40.0 64.0 0.99529 3.12 0.49
## [1087,] 3.0 9.0 0.99738 3.38 0.66
## [1088,] 18.0 30.0 0.99400 3.29 0.69
## [1089,] 22.0 41.0 0.99735 3.02 0.76
## [1090,] 22.0 41.0 0.99735 3.02 0.76
## [1091,] 42.0 74.0 0.99451 2.98 0.63
## [1092,] 8.0 19.0 0.99546 3.35 0.60
## [1093,] 10.0 16.0 0.99479 3.43 0.59
## [1094,] 11.0 31.0 0.99615 3.33 0.86
## [1095,] 9.0 17.0 0.99655 3.35 0.49
## [1096,] 6.0 20.0 0.99772 3.15 0.50
## [1097,] 9.0 17.0 0.99655 3.35 0.49
## [1098,] 5.0 18.0 0.99666 3.20 0.52
## [1099,] 5.0 16.0 0.99358 3.15 0.81
## [1100,] 5.0 18.0 0.99666 3.20 0.52
## [1101,] 23.0 36.0 0.99392 3.11 0.78
## [1102,] 14.0 25.0 0.99388 3.35 0.63
## [1103,] 18.0 30.0 0.99402 3.45 0.54
## [1104,] 14.0 25.0 0.99388 3.35 0.63
## [1105,] 16.0 25.0 0.99360 3.28 0.66
## [1106,] 13.0 49.0 0.99374 3.41 0.60
## [1107,] 9.0 17.0 0.99376 3.21 0.54
## [1108,] 10.0 24.0 0.99523 3.27 0.85
## [1109,] 4.0 13.0 0.99855 3.36 0.49
## [1110,] 27.0 66.0 0.99820 3.17 0.76
## [1111,] 6.0 14.0 0.99593 3.25 0.64
## [1112,] 23.0 55.0 0.99471 3.78 0.64
## [1113,] 6.0 35.0 0.99396 3.29 0.71
## [1114,] 3.0 10.0 0.99698 3.12 0.59
## [1115,] 29.0 80.0 0.99020 3.49 0.66
## [1116,] 15.0 21.0 0.99572 3.38 0.60
## [1117,] 15.0 21.0 0.99572 3.38 0.60
## [1118,] 15.0 21.0 0.99572 3.38 0.60
## [1119,] 14.0 24.0 0.99252 3.30 0.53
## [1120,] 7.0 15.0 0.99256 3.52 0.58
## [1121,] 8.0 17.0 0.99235 3.20 0.72
## [1122,] 21.0 28.0 0.99352 3.44 0.55
## [1123,] 27.0 33.0 0.99220 3.45 0.48
## [1124,] 17.0 29.0 0.99674 3.12 0.65
## [1125,] 3.0 13.0 0.99557 3.62 0.62
## [1126,] 13.0 27.0 0.99394 3.14 0.59
## [1127,] 3.0 11.0 0.99150 3.39 0.54
## [1128,] 26.0 52.0 0.99379 3.51 0.60
## [1129,] 28.0 89.0 0.99840 3.22 0.68
## [1130,] 24.0 70.0 0.99798 3.21 0.69
## [1131,] 5.0 10.0 0.99770 3.18 0.63
## [1132,] 57.0 135.0 0.99341 3.32 0.44
## [1133,] 18.0 38.0 0.99330 3.38 0.88
## [1134,] 10.0 18.0 0.99684 3.37 0.68
## [1135,] 6.0 15.0 0.99524 3.30 0.74
## [1136,] 22.0 50.0 0.99460 3.38 0.60
## [1137,] 6.0 13.0 0.99774 3.22 0.76
## [1138,] 6.0 13.0 0.99774 3.22 0.76
## [1139,] 29.0 94.0 0.99786 3.14 0.58
## [1140,] 16.0 86.0 0.99764 3.34 0.64
## [1141,] 33.0 79.0 0.99690 3.41 0.65
## [1142,] 24.0 71.0 0.99624 3.27 0.85
## [1143,] 6.0 12.0 0.99354 3.30 0.65
## [1144,] 16.0 20.0 0.99672 3.61 0.82
## [1145,] 35.0 70.0 0.99588 3.43 0.62
## [1146,] 31.0 51.0 0.99672 3.53 0.81
## [1147,] 6.0 21.0 0.99600 3.28 0.87
## [1148,] 6.0 14.0 0.99702 3.21 0.49
## [1149,] 9.0 21.0 0.99526 3.39 0.66
## [1150,] 6.0 11.0 0.99585 3.23 0.52
## [1151,] 4.0 12.0 0.99473 3.30 0.76
## [1152,] 16.0 70.0 0.99352 3.46 0.65
## [1153,] 9.0 38.0 0.99616 3.15 0.53
## [1154,] 17.0 38.0 0.99622 3.23 0.66
## [1155,] 50.0 63.0 0.99544 3.59 0.68
## [1156,] 9.0 38.0 0.99616 3.15 0.53
## [1157,] 45.0 88.0 0.99524 3.33 0.76
## [1158,] 16.0 101.0 0.99240 3.46 0.87
## [1159,] 22.0 54.0 0.99572 3.42 1.16
## [1160,] 11.0 37.0 0.99728 3.16 0.67
## [1161,] 6.0 20.0 0.99676 3.14 0.72
## [1162,] 12.0 21.0 0.99551 3.21 0.75
## [1163,] 12.0 19.0 0.99434 3.14 0.71
## [1164,] 10.0 21.0 0.99692 3.29 0.67
## [1165,] 10.0 21.0 0.99692 3.29 0.67
## [1166,] 15.0 38.0 0.99634 3.01 1.10
## [1167,] 7.0 60.0 0.99709 2.94 0.98
## [1168,] 29.0 48.0 0.99528 3.32 0.88
## [1169,] 8.0 44.0 0.99384 3.21 0.56
## [1170,] 5.0 14.0 0.99502 3.32 0.62
## [1171,] 5.0 12.0 0.99667 3.20 0.67
## [1172,] 26.0 44.0 0.99522 3.42 0.68
## [1173,] 5.0 16.0 0.99649 3.27 0.74
## [1174,] 26.0 65.0 0.99716 3.46 0.62
## [1175,] 26.0 65.0 0.99716 3.46 0.62
## [1176,] 48.0 59.0 0.99541 3.61 0.70
## [1177,] 23.0 47.0 0.99572 3.58 0.50
## [1178,] 6.0 11.0 0.99318 3.35 0.66
## [1179,] 17.0 78.0 0.99346 3.68 0.73
## [1180,] 11.0 47.0 0.99599 3.27 0.81
## [1181,] 11.0 47.0 0.99599 3.27 0.81
## [1182,] 5.0 11.0 0.99478 3.19 0.46
## [1183,] 41.0 54.0 0.99754 3.38 0.86
## [1184,] 16.0 61.0 0.99572 3.29 0.60
## [1185,] 11.0 119.0 0.99538 3.36 0.70
## [1186,] 6.0 39.0 0.99346 3.33 0.46
## [1187,] 6.0 12.0 0.99630 3.52 0.50
## [1188,] 6.0 39.0 0.99346 3.33 0.46
## [1189,] 11.0 119.0 0.99538 3.36 0.70
## [1190,] 5.0 19.0 0.99616 3.30 0.44
## [1191,] 6.0 20.0 0.99652 3.28 0.57
## [1192,] 6.0 12.0 0.99551 3.56 0.51
## [1193,] 11.0 41.0 0.99439 3.52 0.80
## [1194,] 6.0 12.0 0.99551 3.56 0.51
## [1195,] 15.0 64.0 0.99588 3.22 0.59
## [1196,] 21.0 56.0 0.99633 3.52 0.60
## [1197,] 23.0 94.0 0.99686 3.21 0.58
## [1198,] 4.0 9.0 0.99458 3.16 0.54
## [1199,] 19.0 77.0 0.99510 3.15 0.79
## [1200,] 23.0 94.0 0.99686 3.21 0.58
## [1201,] 4.0 9.0 0.99458 3.16 0.54
## [1202,] 5.0 10.0 0.99419 3.27 0.54
## [1203,] 6.0 12.0 0.99516 3.35 0.69
## [1204,] 33.0 76.0 0.99878 3.14 0.55
## [1205,] 24.0 44.0 0.99534 3.40 0.85
## [1206,] 24.0 44.0 0.99534 3.40 0.85
## [1207,] 24.0 44.0 0.99534 3.40 0.85
## [1208,] 24.0 52.0 0.99752 3.35 0.94
## [1209,] 24.0 44.0 0.99534 3.40 0.85
## [1210,] 14.0 24.0 0.99428 3.45 0.87
## [1211,] 8.0 15.0 0.99498 3.35 0.62
## [1212,] 22.0 53.0 0.99570 3.32 0.62
## [1213,] 8.0 15.0 0.99498 3.35 0.62
## [1214,] 5.0 13.0 0.99659 3.15 0.75
## [1215,] 6.0 9.0 0.99628 3.10 0.48
## [1216,] 20.0 29.0 0.99488 3.26 0.56
## [1217,] 10.0 79.0 0.99677 3.29 0.69
## [1218,] 43.0 74.0 0.99408 3.23 0.81
## [1219,] 8.0 24.0 0.99600 3.24 0.69
## [1220,] 25.0 50.0 0.99478 3.20 0.83
## [1221,] 17.0 44.0 0.99734 3.28 0.77
## [1222,] 17.0 44.0 0.99734 3.28 0.77
## [1223,] 33.0 89.0 0.99678 3.26 0.46
## [1224,] 9.0 23.0 0.99638 3.23 0.76
## [1225,] 8.0 20.0 0.99920 3.07 0.82
## [1226,] 18.0 77.0 0.99922 3.15 0.51
## [1227,] 27.0 46.0 0.99592 3.02 0.47
## [1228,] 8.0 21.0 0.99769 3.27 0.72
## [1229,] 18.0 88.0 0.99157 3.68 0.73
## [1230,] 19.0 66.0 0.99718 3.40 0.64
## [1231,] 15.0 58.0 0.99470 3.37 0.78
## [1232,] 48.0 90.0 0.99621 3.38 0.62
## [1233,] 19.0 66.0 0.99718 3.40 0.64
## [1234,] 14.0 36.0 0.99614 3.23 0.49
## [1235,] 11.0 18.0 0.99242 3.39 0.40
## [1236,] 6.0 113.0 0.99572 3.30 0.56
## [1237,] 7.0 17.0 0.99659 3.26 0.64
## [1238,] 11.0 18.0 0.99242 3.39 0.40
## [1239,] 12.0 24.0 0.99798 3.38 0.46
## [1240,] 11.0 20.0 0.99488 3.45 0.56
## [1241,] 36.0 60.0 0.99494 3.10 0.40
## [1242,] 28.0 65.0 0.99729 3.16 0.59
## [1243,] 15.0 40.0 0.99414 3.22 0.60
## [1244,] 10.0 86.0 0.99830 3.37 0.62
## [1245,] 72.0 160.0 0.99721 3.33 0.54
## [1246,] 15.0 34.0 0.99655 3.49 0.68
## [1247,] 15.0 48.0 0.99502 3.12 0.48
## [1248,] 15.0 34.0 0.99655 3.49 0.68
## [1249,] 22.0 36.0 0.99490 3.41 0.75
## [1250,] 24.0 37.0 0.99514 3.40 0.61
## [1251,] 24.0 37.0 0.99514 3.40 0.61
## [1252,] 27.0 60.0 0.99630 3.28 0.59
## [1253,] 6.0 14.0 0.99627 3.45 0.58
## [1254,] 6.0 13.0 0.99569 3.43 0.58
## [1255,] 20.0 35.0 0.99628 3.40 0.69
## [1256,] 26.0 46.0 0.99358 3.47 0.45
## [1257,] 43.0 60.0 0.99499 3.10 0.42
## [1258,] 12.0 69.0 0.99633 3.32 0.70
## [1259,] 15.0 33.0 0.99538 3.44 0.63
## [1260,] 15.0 33.0 0.99538 3.44 0.63
## [1261,] 19.0 56.0 0.99632 3.02 1.15
## [1262,] 17.0 24.0 0.99437 3.59 0.55
## [1263,] 34.0 66.0 0.99726 3.15 0.58
## [1264,] 13.0 26.0 0.99780 3.37 0.46
## [1265,] 26.0 51.0 0.99456 3.38 0.72
## [1266,] 16.0 36.0 0.99564 3.38 0.60
## [1267,] 16.0 36.0 0.99564 3.38 0.60
## [1268,] 13.0 19.0 0.99600 3.10 0.87
## [1269,] 21.0 51.0 0.99668 3.47 0.55
## [1270,] 28.0 87.0 0.99080 3.50 0.82
## [1271,] 26.0 60.0 0.99084 3.70 0.75
## [1272,] 24.0 64.0 0.99350 3.38 0.57
## [1273,] 25.0 44.0 0.99385 3.50 0.53
## [1274,] 34.0 44.0 0.99494 3.10 0.43
## [1275,] 17.0 36.0 0.99440 3.24 0.53
## [1276,] 13.0 81.0 0.99688 3.24 0.54
## [1277,] 3.0 10.0 0.99566 3.28 0.56
## [1278,] 3.0 10.0 0.99636 3.35 0.60
## [1279,] 13.0 81.0 0.99688 3.24 0.54
## [1280,] 3.0 9.0 0.99480 3.14 0.57
## [1281,] 28.0 54.0 0.99560 3.37 0.64
## [1282,] 28.0 54.0 0.99560 3.37 0.64
## [1283,] 9.0 22.0 0.99619 3.33 0.68
## [1284,] 17.0 69.0 0.99734 3.26 0.63
## [1285,] 18.0 36.0 0.99476 3.39 0.56
## [1286,] 20.0 47.0 0.99734 3.15 0.57
## [1287,] 17.0 31.0 0.99328 3.35 0.54
## [1288,] 3.0 7.0 0.99286 3.22 0.37
## [1289,] 20.0 110.0 0.99914 3.30 1.17
## [1290,] 20.0 110.0 0.99914 3.30 1.17
## [1291,] 6.0 12.0 0.99521 3.36 0.59
## [1292,] 25.0 60.0 0.99638 3.29 0.75
## [1293,] 14.0 28.0 0.99362 3.62 0.67
## [1294,] 6.0 12.0 0.99672 3.34 0.49
## [1295,] 25.0 60.0 0.99638 3.29 0.75
## [1296,] 51.0 77.5 0.99558 3.20 0.45
## [1297,] 51.0 77.5 0.99558 3.20 0.45
## [1298,] 15.0 29.0 0.99323 3.35 0.61
## [1299,] 11.0 18.0 0.99191 3.45 0.56
## [1300,] 5.0 9.0 0.99476 3.50 0.40
## [1301,] 15.0 28.0 0.99444 3.78 0.61
## [1302,] 20.0 57.0 0.99598 3.60 0.74
## [1303,] 4.0 15.0 0.99576 3.30 0.80
## [1304,] 28.0 68.0 0.99501 3.36 0.66
## [1305,] 21.0 68.0 0.99550 3.12 0.44
## [1306,] 24.0 88.0 0.99612 3.30 0.53
## [1307,] 22.0 91.0 0.99790 3.29 0.62
## [1308,] 15.0 29.0 0.99524 3.41 0.52
## [1309,] 22.0 91.0 0.99790 3.29 0.62
## [1310,] 27.0 63.0 0.99600 3.28 0.61
## [1311,] 24.0 88.0 0.99612 3.30 0.53
## [1312,] 12.0 27.0 0.99290 3.33 0.59
## [1313,] 14.0 41.0 0.99532 3.34 0.47
## [1314,] 20.0 65.0 0.99558 3.40 0.54
## [1315,] 24.0 69.0 0.99556 3.40 0.53
## [1316,] 17.0 91.0 0.99616 3.26 0.58
## [1317,] 16.0 46.0 0.99258 4.01 0.59
## [1318,] 10.0 41.0 0.99638 3.18 0.69
## [1319,] 17.0 91.0 0.99616 3.26 0.58
## [1320,] 18.0 64.0 0.99652 2.90 1.33
## [1321,] 12.0 88.0 0.99796 3.26 0.66
## [1322,] 16.0 46.0 0.99258 4.01 0.59
## [1323,] 9.0 18.0 0.99392 3.18 0.55
## [1324,] 21.0 55.0 0.99495 3.18 0.82
## [1325,] 18.0 34.0 0.99480 3.39 0.60
## [1326,] 18.0 34.0 0.99480 3.39 0.60
## [1327,] 18.0 34.0 0.99480 3.39 0.60
## [1328,] 18.0 34.0 0.99480 3.39 0.60
## [1329,] 13.0 38.0 0.99550 3.34 0.52
## [1330,] 17.0 91.0 0.99616 3.29 0.56
## [1331,] 17.0 91.0 0.99616 3.29 0.56
## [1332,] 31.0 67.0 0.99668 3.26 0.46
## [1333,] 6.0 25.0 0.99581 3.09 0.43
## [1334,] 12.0 48.0 0.99760 3.18 0.51
## [1335,] 4.0 11.0 0.99608 3.39 0.52
## [1336,] 19.0 40.0 0.99387 3.38 0.66
## [1337,] 15.0 26.0 0.99448 3.36 0.45
## [1338,] 15.0 26.0 0.99448 3.36 0.45
## [1339,] 15.0 26.0 0.99448 3.36 0.45
## [1340,] 13.0 31.0 0.99538 3.36 0.54
## [1341,] 13.0 31.0 0.99538 3.36 0.54
## [1342,] 13.0 31.0 0.99538 3.36 0.54
## [1343,] 17.0 31.0 0.99589 3.37 0.51
## [1344,] 13.0 31.0 0.99538 3.36 0.54
## [1345,] 8.0 20.0 0.99852 3.09 0.53
## [1346,] 10.0 28.0 0.99613 3.25 0.53
## [1347,] 5.0 13.0 0.99472 3.52 0.56
## [1348,] 7.0 12.0 0.99587 3.34 0.39
## [1349,] 7.0 12.0 0.99587 3.34 0.39
## [1350,] 21.0 41.0 0.99518 3.41 0.52
## [1351,] 32.0 73.0 0.99654 3.34 0.57
## [1352,] 8.0 46.0 0.99332 3.32 0.51
## [1353,] 14.0 57.0 0.99690 3.37 0.46
## [1354,] 14.0 57.0 0.99690 3.37 0.46
## [1355,] 7.0 28.0 0.99568 3.35 0.52
## [1356,] 5.0 32.0 0.99464 3.36 0.44
## [1357,] 4.0 28.0 0.99464 3.36 0.44
## [1358,] 27.0 67.0 0.99648 3.60 0.59
## [1359,] 52.0 98.0 0.99736 3.28 0.50
## [1360,] 6.0 28.0 0.99704 3.07 0.65
## [1361,] 11.0 38.0 0.99699 3.24 0.56
## [1362,] 13.0 54.0 0.99724 3.36 0.54
## [1363,] 6.0 28.0 0.99704 3.07 0.65
## [1364,] 11.0 63.0 0.99652 3.29 0.48
## [1365,] 10.0 31.0 0.99500 3.46 0.53
## [1366,] 10.0 42.0 0.99710 3.18 0.62
## [1367,] 10.0 45.0 0.99576 3.24 0.50
## [1368,] 9.0 105.0 0.99725 3.25 1.18
## [1369,] 16.0 74.0 0.99656 3.27 0.50
## [1370,] 4.0 8.0 0.99396 3.33 0.37
## [1371,] 12.0 66.0 0.99623 3.00 1.17
## [1372,] 5.0 14.0 0.99476 3.21 1.03
## [1373,] 12.0 66.0 0.99623 3.00 1.17
## [1374,] 34.0 89.0 0.99664 3.24 0.45
## [1375,] 16.0 29.0 0.99471 3.32 0.51
## [1376,] 13.0 100.0 0.99586 3.26 0.52
## [1377,] 7.0 31.0 0.99460 3.11 0.46
## [1378,] 23.0 74.0 0.99530 3.71 0.62
## [1379,] 10.0 28.0 0.99609 3.29 0.51
## [1380,] 11.0 35.0 0.99557 3.36 0.62
## [1381,] 11.0 35.0 0.99557 3.36 0.62
## [1382,] 4.0 25.0 0.99534 3.38 0.45
## [1383,] 25.0 105.0 0.99613 3.30 0.49
## [1384,] 25.0 105.0 0.99613 3.30 0.49
## [1385,] 20.0 84.0 0.99593 3.19 0.50
## [1386,] 34.0 85.0 0.99668 3.19 0.42
## [1387,] 8.0 23.0 0.99610 3.30 0.58
## [1388,] 8.0 23.0 0.99610 3.30 0.58
## [1389,] 7.0 49.0 0.99718 3.35 0.68
## [1390,] 36.0 111.0 0.99524 3.10 0.53
## [1391,] 15.0 33.0 0.99292 3.58 0.59
## [1392,] 5.0 33.0 0.99612 3.37 0.58
## [1393,] 5.0 12.0 0.99420 3.17 0.48
## [1394,] 19.0 59.0 0.99612 3.30 0.48
## [1395,] 27.0 73.0 0.99669 3.32 0.48
## [1396,] 3.0 12.0 0.99745 3.31 0.65
## [1397,] 4.0 11.0 0.99745 3.31 0.65
## [1398,] 17.0 104.0 0.99584 3.28 0.52
## [1399,] 13.0 28.0 0.99660 3.52 0.62
## [1400,] 10.0 24.0 0.99560 3.42 0.72
## [1401,] 33.0 141.0 0.99620 3.25 0.51
## [1402,] 33.0 141.0 0.99620 3.25 0.51
## [1403,] 6.0 24.0 0.99630 3.44 0.82
## [1404,] 3.0 13.0 0.99600 3.23 1.10
## [1405,] 12.0 46.0 0.99850 3.43 0.62
## [1406,] 18.0 34.0 0.99520 3.28 0.90
## [1407,] 8.0 22.0 0.99740 3.22 0.94
## [1408,] 40.0 54.0 0.99500 3.54 0.93
## [1409,] 35.0 53.0 0.99500 3.27 1.01
## [1410,] 40.0 54.0 0.99500 3.54 0.93
## [1411,] 5.0 16.0 0.99360 3.50 0.44
## [1412,] 8.0 19.0 0.99630 3.56 0.73
## [1413,] 8.0 22.0 0.99740 3.22 0.94
## [1414,] 12.0 89.0 0.99630 3.04 0.90
## [1415,] 3.0 15.0 0.99940 3.20 0.78
## [1416,] 8.0 18.0 0.99660 3.56 0.84
## [1417,] 3.0 15.0 0.99940 3.20 0.78
## [1418,] 21.0 37.0 0.99520 3.35 0.77
## [1419,] 3.0 19.0 0.99500 3.16 0.46
## [1420,] 32.0 133.0 0.99560 3.27 0.45
## [1421,] 3.0 19.0 0.99500 3.16 0.46
## [1422,] 24.0 58.0 0.99650 3.34 0.58
## [1423,] 39.0 55.0 0.99560 3.39 0.84
## [1424,] 14.0 31.0 0.99680 3.50 0.67
## [1425,] 8.0 23.0 0.99740 3.26 0.70
## [1426,] 8.0 23.0 0.99740 3.26 0.70
## [1427,] 23.0 60.0 0.99710 3.41 0.71
## [1428,] 6.0 23.0 0.99570 3.30 0.58
## [1429,] 27.0 55.0 0.99620 3.31 0.63
## [1430,] 38.0 67.0 0.99600 3.33 0.93
## [1431,] 18.0 47.0 0.99560 3.37 0.62
## [1432,] 13.0 74.0 0.99580 3.26 0.54
## [1433,] 3.0 11.0 0.99440 3.48 0.85
## [1434,] 11.0 25.0 0.99550 3.42 0.74
## [1435,] 55.0 95.0 1.00369 3.18 0.77
## [1436,] 55.0 95.0 1.00369 3.18 0.77
## [1437,] 27.0 90.0 0.99914 3.15 0.65
## [1438,] 15.0 39.0 0.99577 3.53 0.54
## [1439,] 3.0 8.0 0.99600 3.41 0.47
## [1440,] 31.0 92.0 0.99566 3.32 0.68
## [1441,] 15.0 28.0 0.99470 3.23 0.73
## [1442,] 19.0 98.0 0.99713 3.16 0.52
## [1443,] 18.0 30.0 0.99712 3.40 0.75
## [1444,] 8.0 22.0 0.99322 3.38 0.49
## [1445,] 31.0 92.0 0.99566 3.32 0.68
## [1446,] 19.0 98.0 0.99713 3.16 0.52
## [1447,] 18.0 30.0 0.99712 3.40 0.75
## [1448,] 22.0 39.0 0.99701 3.40 0.74
## [1449,] 40.0 54.0 0.99683 3.40 0.73
## [1450,] 15.0 29.0 0.99472 3.23 0.76
## [1451,] 15.0 28.0 0.99470 3.23 0.73
## [1452,] 8.0 17.0 0.99732 3.33 0.78
## [1453,] 37.0 53.0 0.99374 3.35 0.76
## [1454,] 27.0 85.0 0.99706 3.41 0.58
## [1455,] 7.0 23.0 0.99974 3.21 0.69
## [1456,] 9.0 17.0 0.99467 3.50 0.63
## [1457,] 38.0 89.0 0.99236 3.30 0.50
## [1458,] 27.0 85.0 0.99706 3.41 0.58
## [1459,] 8.0 20.0 0.99603 3.39 0.60
## [1460,] 7.0 15.0 0.99458 3.32 0.80
## [1461,] 34.0 64.0 0.99724 3.41 0.68
## [1462,] 6.0 13.0 0.99664 3.59 0.61
## [1463,] 14.0 31.0 0.99545 3.36 0.58
## [1464,] 14.0 39.0 0.99522 3.34 0.53
## [1465,] 34.0 53.0 0.99580 3.41 0.67
## [1466,] 34.0 53.0 0.99580 3.41 0.67
## [1467,] 31.0 54.0 0.99728 3.30 0.65
## [1468,] 19.0 32.0 0.99648 3.52 0.57
## [1469,] 31.0 54.0 0.99728 3.30 0.65
## [1470,] 20.0 49.0 0.99705 3.31 0.55
## [1471,] 8.0 24.0 0.99578 2.88 0.47
## [1472,] 8.0 16.0 0.99334 3.43 0.52
## [1473,] 23.0 44.0 0.99656 3.38 0.79
## [1474,] 14.0 45.0 0.99336 3.38 0.54
## [1475,] 48.0 82.0 1.00242 3.16 0.75
## [1476,] 16.0 89.0 0.99182 3.54 0.88
## [1477,] 48.0 82.0 1.00242 3.16 0.75
## [1478,] 16.0 89.0 0.99182 3.54 0.88
## [1479,] 3.0 14.0 0.99808 3.40 0.52
## [1480,] 10.0 22.0 0.99828 3.39 0.68
## [1481,] 6.0 13.0 0.99498 3.66 0.62
## [1482,] 10.0 22.0 0.99828 3.39 0.68
## [1483,] 3.0 9.0 0.99719 3.33 0.57
## [1484,] 6.0 12.0 0.99542 3.35 0.61
## [1485,] 4.0 11.0 0.99592 3.53 0.64
## [1486,] 8.0 25.0 0.99606 3.37 0.55
## [1487,] 9.0 23.0 0.99546 3.38 0.60
## [1488,] 9.0 17.0 0.99496 3.52 0.78
## [1489,] 5.0 13.0 0.99420 3.72 0.58
## [1490,] 4.0 11.0 0.99448 3.44 0.76
## [1491,] 8.0 18.0 0.99344 3.39 0.56
## [1492,] 5.0 13.0 0.99420 3.72 0.58
## [1493,] 6.0 18.0 0.99348 3.57 0.54
## [1494,] 23.0 147.0 0.99636 3.26 0.59
## [1495,] 15.0 28.0 0.99459 3.42 0.70
## [1496,] 15.0 28.0 0.99492 3.35 0.81
## [1497,] 23.0 147.0 0.99636 3.26 0.59
## [1498,] 7.0 16.0 0.99508 3.45 0.63
## [1499,] 7.0 13.0 0.99582 3.53 0.58
## [1500,] 7.0 16.0 0.99508 3.45 0.63
## [1501,] 8.0 15.0 0.99508 3.26 0.53
## [1502,] 21.0 64.0 0.99642 3.16 0.53
## [1503,] 15.0 60.0 0.99638 3.31 0.54
## [1504,] 6.0 14.0 0.99555 3.51 0.69
## [1505,] 5.0 12.0 0.99605 3.36 0.55
## [1506,] 6.0 12.0 0.99600 3.55 0.63
## [1507,] 6.0 14.0 0.99562 3.51 0.66
## [1508,] 5.0 12.0 0.99605 3.36 0.55
## [1509,] 17.0 25.0 0.99814 3.38 0.72
## [1510,] 7.0 20.0 0.99410 3.28 0.70
## [1511,] 26.0 48.0 0.99661 3.47 0.77
## [1512,] 19.0 27.0 0.99712 3.44 0.67
## [1513,] 11.0 17.0 0.99588 3.53 0.65
## [1514,] 17.0 65.0 0.99294 3.33 0.60
## [1515,] 16.0 65.0 0.99842 3.53 0.72
## [1516,] 16.0 65.0 0.99842 3.53 0.72
## [1517,] 23.0 58.0 0.99633 3.42 0.97
## [1518,] 29.0 44.0 0.99489 3.38 0.83
## [1519,] 7.0 20.0 0.99647 3.32 0.63
## [1520,] 14.0 27.0 0.99665 3.44 0.58
## [1521,] 29.0 44.0 0.99489 3.38 0.83
## [1522,] 9.0 23.0 0.99585 3.39 0.43
## [1523,] 23.0 58.0 0.99633 3.42 0.97
## [1524,] 29.0 61.0 0.99530 3.34 0.60
## [1525,] 22.0 47.0 0.99522 3.39 0.78
## [1526,] 18.0 34.0 0.99552 3.33 0.64
## [1527,] 18.0 38.0 0.99553 3.30 0.65
## [1528,] 15.0 24.0 0.99510 3.29 0.66
## [1529,] 21.0 59.0 0.99714 3.31 0.67
## [1530,] 42.0 52.0 0.99608 3.42 0.60
## [1531,] 9.0 18.0 0.99444 3.42 0.69
## [1532,] 13.0 28.0 0.99631 3.60 0.66
## [1533,] 18.0 22.0 0.99573 3.21 0.68
## [1534,] 32.0 84.0 0.99717 3.39 0.61
## [1535,] 13.0 29.0 0.99397 3.42 0.62
## [1536,] 15.0 35.0 0.99590 3.36 0.59
## [1537,] 24.0 45.0 0.99528 3.60 0.68
## [1538,] 20.0 31.0 0.99484 3.50 0.64
## [1539,] 15.0 26.0 0.99538 3.57 0.63
## [1540,] 34.0 60.0 0.99714 3.46 0.78
## [1541,] 11.0 32.0 0.99646 3.56 0.63
## [1542,] 19.0 49.0 0.99666 3.40 0.76
## [1543,] 29.0 38.0 0.99472 3.30 0.56
## [1544,] 14.0 19.0 0.99758 3.25 0.57
## [1545,] 12.0 19.0 0.99550 3.17 0.81
## [1546,] 16.0 28.0 0.99531 3.36 0.64
## [1547,] 17.0 26.0 0.99575 3.36 0.61
## [1548,] 12.0 26.0 0.99306 3.55 0.51
## [1549,] 19.0 50.0 0.99783 3.10 0.58
## [1550,] 17.0 24.0 0.99419 3.24 0.70
## [1551,] 17.0 26.0 0.99783 3.45 0.53
## [1552,] 18.0 27.0 0.99768 3.44 0.54
## [1553,] 32.0 54.0 0.99586 3.51 0.66
## [1554,] 18.0 28.0 0.99765 3.41 0.60
## [1555,] 15.0 23.0 0.99627 3.54 0.60
## [1556,] 15.0 24.0 0.99514 3.44 0.68
## [1557,] 12.0 20.0 0.99636 3.53 0.56
## [1558,] 15.0 23.0 0.99627 3.54 0.60
## [1559,] 66.0 115.0 0.99787 3.22 0.56
## [1560,] 31.0 131.0 0.99622 3.21 0.52
## [1561,] 31.0 131.0 0.99622 3.21 0.52
## [1562,] 31.0 131.0 0.99622 3.21 0.52
## [1563,] 12.0 20.0 0.99546 3.29 0.54
## [1564,] 12.0 20.0 0.99546 3.29 0.54
## [1565,] 12.0 20.0 0.99546 3.29 0.54
## [1566,] 26.0 42.0 0.99489 3.39 0.82
## [1567,] 24.0 52.0 0.99494 3.34 0.71
## [1568,] 12.0 20.0 0.99546 3.29 0.54
## [1569,] 25.0 42.0 0.99629 3.34 0.59
## [1570,] 15.0 34.0 0.99396 3.48 0.57
## [1571,] 19.0 35.0 0.99340 3.37 0.93
## [1572,] 15.0 25.0 0.99514 3.44 0.65
## [1573,] 35.0 104.0 0.99632 3.33 0.51
## [1574,] 15.0 50.0 0.99467 3.58 0.67
## [1575,] 23.0 92.0 0.99677 3.39 0.48
## [1576,] 12.0 20.0 0.99474 3.26 0.64
## [1577,] 16.0 29.0 0.99588 3.30 0.78
## [1578,] 13.0 27.0 0.99622 3.54 0.60
## [1579,] 13.0 20.0 0.99540 3.42 0.67
## [1580,] 24.0 32.0 0.99402 3.54 0.60
## [1581,] 9.0 26.0 0.99470 3.36 0.60
## [1582,] 24.0 32.0 0.99402 3.54 0.60
## [1583,] 13.0 27.0 0.99362 3.57 0.50
## [1584,] 32.0 98.0 0.99578 3.33 0.62
## [1585,] 24.0 34.0 0.99484 3.29 0.80
## [1586,] 22.0 48.0 0.99494 3.30 0.84
## [1587,] 34.0 60.0 0.99492 3.34 0.85
## [1588,] 18.0 28.0 0.99483 3.55 0.66
## [1589,] 34.0 102.0 0.99414 3.27 0.78
## [1590,] 29.0 79.0 0.99770 3.29 0.54
## [1591,] 26.0 35.0 0.99314 3.32 0.82
## [1592,] 16.0 26.0 0.99402 3.67 0.56
## [1593,] 29.0 40.0 0.99574 3.42 0.75
## [1594,] 28.0 38.0 0.99651 3.42 0.82
## [1595,] 32.0 44.0 0.99490 3.45 0.58
## [1596,] 39.0 51.0 0.99512 3.52 0.76
## [1597,] 29.0 40.0 0.99574 3.42 0.75
## [1598,] 32.0 44.0 0.99547 3.57 0.71
## [1599,] 18.0 42.0 0.99549 3.39 0.66
## alcohol
## [1,] 9.400000 0
## [2,] 9.800000 0
## [3,] 9.800000 0
## [4,] 9.800000 1
## [5,] 9.400000 0
## [6,] 9.400000 0
## [7,] 9.400000 0
## [8,] 10.000000 1
## [9,] 9.500000 1
## [10,] 10.500000 0
## [11,] 9.200000 0
## [12,] 10.500000 0
## [13,] 9.900000 0
## [14,] 9.100000 0
## [15,] 9.200000 0
## [16,] 9.200000 0
## [17,] 10.500000 1
## [18,] 9.300000 0
## [19,] 9.000000 0
## [20,] 9.200000 1
## [21,] 9.400000 1
## [22,] 9.700000 0
## [23,] 9.500000 0
## [24,] 9.400000 0
## [25,] 9.700000 1
## [26,] 9.300000 0
## [27,] 9.500000 0
## [28,] 9.500000 0
## [29,] 9.400000 0
## [30,] 9.800000 1
## [31,] 10.100000 0
## [32,] 10.600000 1
## [33,] 9.800000 0
## [34,] 9.400000 1
## [35,] 9.200000 0
## [36,] 9.600000 1
## [37,] 10.800000 1
## [38,] 9.700000 1
## [39,] 9.800000 0
## [40,] 10.500000 0
## [41,] 10.500000 0
## [42,] 9.300000 0
## [43,] 10.500000 1
## [44,] 10.300000 0
## [45,] 9.500000 0
## [46,] 13.100000 0
## [47,] 9.200000 0
## [48,] 9.500000 0
## [49,] 9.200000 0
## [50,] 9.200000 0
## [51,] 9.200000 0
## [52,] 9.400000 1
## [53,] 9.400000 1
## [54,] 9.400000 0
## [55,] 10.200000 1
## [56,] 9.500000 0
## [57,] 9.600000 0
## [58,] 9.400000 0
## [59,] 10.000000 0
## [60,] 9.400000 1
## [61,] 9.200000 0
## [62,] 9.300000 0
## [63,] 9.500000 1
## [64,] 9.800000 0
## [65,] 10.900000 0
## [66,] 10.900000 0
## [67,] 9.600000 0
## [68,] 10.700000 0
## [69,] 10.700000 0
## [70,] 10.500000 1
## [71,] 9.500000 1
## [72,] 9.500000 0
## [73,] 9.500000 0
## [74,] 9.200000 0
## [75,] 9.600000 0
## [76,] 10.500000 0
## [77,] 10.500000 0
## [78,] 10.700000 1
## [79,] 10.100000 0
## [80,] 9.100000 0
## [81,] 9.200000 0
## [82,] 9.400000 0
## [83,] 9.100000 0
## [84,] 9.400000 0
## [85,] 10.300000 1
## [86,] 10.100000 0
## [87,] 9.900000 1
## [88,] 9.600000 0
## [89,] 9.500000 0
## [90,] 9.000000 0
## [91,] 9.500000 0
## [92,] 9.900000 1
## [93,] 9.800000 0
## [94,] 9.600000 0
## [95,] 10.500000 0
## [96,] 12.900000 1
## [97,] 10.700000 0
## [98,] 9.200000 0
## [99,] 9.800000 0
## [100,] 9.000000 1
## [101,] 10.200000 1
## [102,] 10.400000 1
## [103,] 9.000000 1
## [104,] 9.200000 0
## [105,] 9.400000 0
## [106,] 9.200000 0
## [107,] 9.300000 0
## [108,] 9.300000 0
## [109,] 9.600000 1
## [110,] 9.300000 0
## [111,] 9.500000 0
## [112,] 9.800000 0
## [113,] 9.800000 0
## [114,] 9.700000 1
## [115,] 9.500000 0
## [116,] 10.500000 1
## [117,] 10.000000 1
## [118,] 9.400000 1
## [119,] 10.900000 1
## [120,] 9.200000 1
## [121,] 9.000000 0
## [122,] 10.900000 1
## [123,] 9.200000 0
## [124,] 9.500000 0
## [125,] 9.500000 0
## [126,] 9.400000 0
## [127,] 10.900000 0
## [128,] 10.900000 0
## [129,] 10.500000 1
## [130,] 9.400000 0
## [131,] 9.400000 0
## [132,] 13.000000 0
## [133,] 13.000000 0
## [134,] 9.800000 1
## [135,] 9.900000 1
## [136,] 9.600000 0
## [137,] 9.500000 0
## [138,] 9.200000 0
## [139,] 9.500000 0
## [140,] 9.500000 0
## [141,] 9.600000 0
## [142,] 9.500000 0
## [143,] 14.000000 1
## [144,] 9.400000 0
## [145,] 14.000000 1
## [146,] 9.400000 0
## [147,] 10.000000 0
## [148,] 9.300000 0
## [149,] 10.200000 1
## [150,] 10.500000 1
## [151,] 10.300000 1
## [152,] 9.400000 0
## [153,] 10.100000 0
## [154,] 10.100000 0
## [155,] 10.500000 0
## [156,] 10.500000 0
## [157,] 10.500000 0
## [158,] 10.500000 0
## [159,] 9.300000 0
## [160,] 9.300000 1
## [161,] 9.600000 0
## [162,] 9.200000 0
## [163,] 10.000000 1
## [164,] 9.400000 0
## [165,] 9.400000 0
## [166,] 9.500000 0
## [167,] 10.200000 0
## [168,] 9.000000 0
## [169,] 10.400000 1
## [170,] 9.500000 0
## [171,] 9.100000 0
## [172,] 9.200000 1
## [173,] 9.200000 1
## [174,] 11.500000 1
## [175,] 9.500000 0
## [176,] 9.500000 0
## [177,] 9.500000 0
## [178,] 10.500000 1
## [179,] 9.600000 0
## [180,] 9.500000 0
## [181,] 9.500000 0
## [182,] 9.300000 0
## [183,] 9.300000 0
## [184,] 9.300000 0
## [185,] 9.300000 1
## [186,] 9.700000 0
## [187,] 9.200000 0
## [188,] 9.700000 0
## [189,] 9.500000 0
## [190,] 9.500000 0
## [191,] 9.400000 0
## [192,] 9.800000 1
## [193,] 9.500000 0
## [194,] 9.700000 0
## [195,] 9.700000 0
## [196,] 9.400000 0
## [197,] 10.200000 0
## [198,] 10.100000 1
## [199,] 13.000000 1
## [200,] 11.400000 0
## [201,] 10.300000 1
## [202,] 9.300000 0
## [203,] 9.500000 0
## [204,] 9.200000 0
## [205,] 9.200000 1
## [206,] 10.800000 1
## [207,] 10.800000 1
## [208,] 9.300000 0
## [209,] 9.400000 0
## [210,] 10.500000 1
## [211,] 12.400000 1
## [212,] 10.000000 1
## [213,] 10.200000 1
## [214,] 10.100000 0
## [215,] 9.800000 1
## [216,] 10.500000 0
## [217,] 11.000000 0
## [218,] 9.100000 0
## [219,] 9.700000 0
## [220,] 9.500000 0
## [221,] 9.400000 1
## [222,] 9.400000 0
## [223,] 9.500000 0
## [224,] 10.000000 1
## [225,] 10.400000 0
## [226,] 10.500000 1
## [227,] 9.500000 1
## [228,] 9.800000 0
## [229,] 10.500000 1
## [230,] 11.000000 0
## [231,] 12.200000 1
## [232,] 9.900000 1
## [233,] 9.600000 1
## [234,] 11.000000 0
## [235,] 9.000000 1
## [236,] 9.000000 1
## [237,] 9.000000 1
## [238,] 9.200000 1
## [239,] 9.000000 1
## [240,] 9.000000 1
## [241,] 9.300000 0
## [242,] 10.900000 1
## [243,] 9.800000 1
## [244,] 9.200000 1
## [245,] 9.200000 1
## [246,] 9.900000 1
## [247,] 9.500000 0
## [248,] 9.300000 0
## [249,] 9.800000 1
## [250,] 9.900000 1
## [251,] 10.000000 1
## [252,] 9.900000 1
## [253,] 10.500000 0
## [254,] 9.500000 0
## [255,] 9.900000 1
## [256,] 9.300000 0
## [257,] 9.200000 0
## [258,] 9.200000 0
## [259,] 9.400000 0
## [260,] 10.500000 1
## [261,] 9.300000 0
## [262,] 9.400000 0
## [263,] 10.000000 0
## [264,] 9.300000 0
## [265,] 10.900000 0
## [266,] 10.200000 1
## [267,] 9.800000 0
## [268,] 12.800000 1
## [269,] 9.400000 1
## [270,] 10.100000 1
## [271,] 10.700000 1
## [272,] 10.100000 1
## [273,] 10.100000 0
## [274,] 9.400000 0
## [275,] 9.400000 0
## [276,] 10.700000 1
## [277,] 9.400000 1
## [278,] 10.100000 1
## [279,] 12.600000 1
## [280,] 10.500000 1
## [281,] 9.300000 1
## [282,] 9.900000 1
## [283,] 9.100000 0
## [284,] 10.500000 1
## [285,] 9.800000 0
## [286,] 9.800000 0
## [287,] 10.300000 1
## [288,] 10.300000 1
## [289,] 10.600000 1
## [290,] 9.200000 0
## [291,] 10.600000 1
## [292,] 10.500000 0
## [293,] 10.300000 1
## [294,] 10.100000 1
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## [977,] 9.400000 0
## [978,] 9.100000 0
## [979,] 11.300000 1
## [980,] 10.400000 0
## [981,] 10.500000 1
## [982,] 10.000000 0
## [983,] 12.900000 1
## [984,] 10.500000 1
## [985,] 10.400000 0
## [986,] 11.300000 1
## [987,] 11.400000 1
## [988,] 9.400000 0
## [989,] 9.400000 0
## [990,] 10.900000 1
## [991,] 9.400000 0
## [992,] 9.400000 0
## [993,] 9.400000 1
## [994,] 9.400000 0
## [995,] 9.400000 0
## [996,] 10.100000 1
## [997,] 12.800000 1
## [998,] 12.800000 1
## [999,] 9.100000 1
## [1000,] 12.900000 1
## [1001,] 11.500000 1
## [1002,] 10.600000 1
## [1003,] 11.700000 1
## [1004,] 12.800000 1
## [1005,] 10.300000 0
## [1006,] 12.800000 1
## [1007,] 11.700000 1
## [1008,] 11.700000 1
## [1009,] 12.000000 1
## [1010,] 10.900000 0
## [1011,] 12.300000 1
## [1012,] 10.400000 1
## [1013,] 10.000000 0
## [1014,] 10.000000 1
## [1015,] 11.200000 1
## [1016,] 11.400000 1
## [1017,] 12.600000 1
## [1018,] 12.700000 1
## [1019,] 12.700000 1
## [1020,] 10.400000 0
## [1021,] 11.900000 1
## [1022,] 11.900000 1
## [1023,] 10.500000 0
## [1024,] 12.300000 1
## [1025,] 10.500000 1
## [1026,] 10.400000 1
## [1027,] 12.600000 1
## [1028,] 11.600000 0
## [1029,] 10.100000 1
## [1030,] 10.500000 1
## [1031,] 11.500000 1
## [1032,] 11.000000 1
## [1033,] 9.600000 0
## [1034,] 10.400000 1
## [1035,] 9.700000 1
## [1036,] 10.600000 1
## [1037,] 12.500000 1
## [1038,] 9.200000 0
## [1039,] 12.600000 1
## [1040,] 11.100000 1
## [1041,] 10.200000 0
## [1042,] 9.800000 1
## [1043,] 11.100000 1
## [1044,] 10.900000 1
## [1045,] 12.200000 1
## [1046,] 11.400000 1
## [1047,] 10.700000 1
## [1048,] 10.400000 0
## [1049,] 10.900000 1
## [1050,] 10.800000 1
## [1051,] 10.400000 0
## [1052,] 9.200000 0
## [1053,] 12.900000 0
## [1054,] 12.700000 1
## [1055,] 9.100000 1
## [1056,] 9.100000 1
## [1057,] 12.100000 1
## [1058,] 9.100000 0
## [1059,] 11.600000 1
## [1060,] 12.100000 1
## [1061,] 9.900000 1
## [1062,] 12.500000 1
## [1063,] 11.400000 1
## [1064,] 11.800000 1
## [1065,] 11.800000 1
## [1066,] 10.200000 1
## [1067,] 12.500000 1
## [1068,] 10.900000 1
## [1069,] 10.900000 1
## [1070,] 10.800000 0
## [1071,] 11.100000 1
## [1072,] 9.200000 0
## [1073,] 9.500000 1
## [1074,] 10.700000 1
## [1075,] 9.200000 0
## [1076,] 10.200000 1
## [1077,] 11.400000 1
## [1078,] 11.000000 0
## [1079,] 11.000000 0
## [1080,] 12.300000 1
## [1081,] 11.800000 1
## [1082,] 12.300000 1
## [1083,] 9.500000 1
## [1084,] 12.000000 1
## [1085,] 9.500000 1
## [1086,] 9.600000 0
## [1087,] 11.600000 1
## [1088,] 11.200000 1
## [1089,] 9.900000 1
## [1090,] 9.900000 1
## [1091,] 11.800000 1
## [1092,] 11.400000 1
## [1093,] 11.500000 1
## [1094,] 12.000000 1
## [1095,] 10.800000 1
## [1096,] 10.500000 0
## [1097,] 10.800000 1
## [1098,] 9.400000 0
## [1099,] 12.500000 1
## [1100,] 9.400000 0
## [1101,] 12.400000 1
## [1102,] 12.000000 1
## [1103,] 11.200000 1
## [1104,] 12.000000 1
## [1105,] 12.400000 1
## [1106,] 12.800000 0
## [1107,] 12.300000 1
## [1108,] 11.700000 1
## [1109,] 9.500000 0
## [1110,] 10.800000 1
## [1111,] 10.000000 1
## [1112,] 12.300000 1
## [1113,] 11.000000 1
## [1114,] 9.500000 1
## [1115,] 13.600000 1
## [1116,] 11.300000 1
## [1117,] 11.300000 1
## [1118,] 11.300000 1
## [1119,] 13.300000 1
## [1120,] 12.900000 0
## [1121,] 13.100000 1
## [1122,] 12.300000 1
## [1123,] 12.300000 1
## [1124,] 11.200000 1
## [1125,] 11.500000 0
## [1126,] 11.300000 1
## [1127,] 13.500000 1
## [1128,] 11.500000 1
## [1129,] 10.000000 0
## [1130,] 10.500000 1
## [1131,] 10.400000 1
## [1132,] 9.500000 0
## [1133,] 13.600000 1
## [1134,] 11.200000 1
## [1135,] 11.800000 1
## [1136,] 11.900000 1
## [1137,] 11.400000 1
## [1138,] 11.400000 1
## [1139,] 9.100000 0
## [1140,] 9.500000 1
## [1141,] 9.500000 1
## [1142,] 11.000000 1
## [1143,] 11.400000 1
## [1144,] 10.000000 1
## [1145,] 10.100000 0
## [1146,] 10.400000 1
## [1147,] 9.800000 1
## [1148,] 11.800000 1
## [1149,] 11.600000 1
## [1150,] 12.000000 1
## [1151,] 12.800000 1
## [1152,] 12.500000 1
## [1153,] 9.800000 0
## [1154,] 11.100000 1
## [1155,] 11.400000 1
## [1156,] 9.800000 0
## [1157,] 11.800000 1
## [1158,] 12.900000 1
## [1159,] 10.600000 1
## [1160,] 10.800000 0
## [1161,] 11.100000 1
## [1162,] 10.200000 1
## [1163,] 11.800000 1
## [1164,] 10.000000 0
## [1165,] 10.000000 0
## [1166,] 9.400000 0
## [1167,] 10.200000 0
## [1168,] 12.400000 1
## [1169,] 12.000000 1
## [1170,] 11.500000 1
## [1171,] 10.500000 1
## [1172,] 10.800000 1
## [1173,] 12.300000 1
## [1174,] 9.500000 1
## [1175,] 9.500000 1
## [1176,] 11.500000 1
## [1177,] 11.200000 0
## [1178,] 12.700000 1
## [1179,] 11.400000 0
## [1180,] 11.000000 1
## [1181,] 11.000000 1
## [1182,] 11.400000 0
## [1183,] 10.500000 1
## [1184,] 9.300000 0
## [1185,] 10.900000 0
## [1186,] 11.900000 1
## [1187,] 12.200000 0
## [1188,] 11.900000 1
## [1189,] 10.900000 0
## [1190,] 9.600000 0
## [1191,] 12.500000 1
## [1192,] 10.800000 0
## [1193,] 12.400000 1
## [1194,] 10.800000 0
## [1195,] 9.500000 1
## [1196,] 9.500000 1
## [1197,] 9.500000 1
## [1198,] 9.800000 1
## [1199,] 10.900000 1
## [1200,] 9.500000 1
## [1201,] 9.800000 1
## [1202,] 11.200000 1
## [1203,] 11.700000 1
## [1204,] 9.400000 0
## [1205,] 11.000000 1
## [1206,] 11.000000 1
## [1207,] 11.000000 1
## [1208,] 10.000000 0
## [1209,] 11.000000 1
## [1210,] 11.200000 1
## [1211,] 10.400000 1
## [1212,] 9.300000 0
## [1213,] 10.400000 1
## [1214,] 10.500000 1
## [1215,] 10.400000 1
## [1216,] 11.300000 1
## [1217,] 9.500000 1
## [1218,] 12.000000 1
## [1219,] 10.600000 1
## [1220,] 10.900000 1
## [1221,] 11.500000 1
## [1222,] 11.500000 1
## [1223,] 9.600000 1
## [1224,] 12.000000 1
## [1225,] 10.300000 1
## [1226,] 9.400000 0
## [1227,] 9.200000 0
## [1228,] 9.600000 0
## [1229,] 13.600000 1
## [1230,] 9.500000 0
## [1231,] 11.800000 1
## [1232,] 10.800000 0
## [1233,] 9.500000 0
## [1234,] 9.300000 0
## [1235,] 12.800000 1
## [1236,] 11.500000 0
## [1237,] 9.400000 1
## [1238,] 12.800000 1
## [1239,] 9.600000 0
## [1240,] 11.800000 0
## [1241,] 9.300000 0
## [1242,] 9.800000 0
## [1243,] 12.200000 1
## [1244,] 9.500000 0
## [1245,] 10.300000 1
## [1246,] 10.500000 0
## [1247,] 10.000000 0
## [1248,] 10.500000 0
## [1249,] 11.100000 1
## [1250,] 10.900000 1
## [1251,] 10.900000 1
## [1252,] 9.800000 0
## [1253,] 9.800000 0
## [1254,] 9.500000 0
## [1255,] 10.900000 0
## [1256,] 11.000000 0
## [1257,] 9.200000 0
## [1258,] 11.000000 1
## [1259,] 11.300000 1
## [1260,] 11.300000 1
## [1261,] 9.300000 0
## [1262,] 11.200000 0
## [1263,] 9.800000 0
## [1264,] 9.600000 0
## [1265,] 11.800000 1
## [1266,] 10.300000 1
## [1267,] 10.300000 1
## [1268,] 11.400000 1
## [1269,] 9.500000 1
## [1270,] 14.000000 1
## [1271,] 14.000000 1
## [1272,] 11.700000 1
## [1273,] 11.200000 0
## [1274,] 9.300000 0
## [1275,] 11.200000 1
## [1276,] 9.500000 1
## [1277,] 12.000000 0
## [1278,] 9.700000 1
## [1279,] 9.500000 1
## [1280,] 11.500000 1
## [1281,] 10.400000 1
## [1282,] 10.400000 1
## [1283,] 10.900000 1
## [1284,] 10.200000 1
## [1285,] 10.900000 0
## [1286,] 10.500000 0
## [1287,] 12.400000 1
## [1288,] 13.000000 0
## [1289,] 10.200000 0
## [1290,] 10.200000 0
## [1291,] 11.000000 0
## [1292,] 10.900000 1
## [1293,] 12.400000 1
## [1294,] 9.700000 0
## [1295,] 10.900000 1
## [1296,] 9.500000 0
## [1297,] 9.500000 0
## [1298,] 12.100000 1
## [1299,] 12.200000 1
## [1300,] 10.900000 0
## [1301,] 12.500000 1
## [1302,] 11.700000 1
## [1303,] 11.200000 1
## [1304,] 11.200000 0
## [1305,] 9.200000 0
## [1306,] 9.800000 0
## [1307,] 10.100000 0
## [1308,] 11.100000 0
## [1309,] 10.100000 0
## [1310,] 9.200000 0
## [1311,] 9.800000 0
## [1312,] 12.800000 1
## [1313,] 10.500000 0
## [1314,] 10.100000 1
## [1315,] 10.100000 1
## [1316,] 9.800000 1
## [1317,] 12.500000 1
## [1318,] 11.900000 1
## [1319,] 9.800000 1
## [1320,] 9.100000 1
## [1321,] 10.100000 0
## [1322,] 12.500000 1
## [1323,] 11.400000 0
## [1324,] 11.000000 1
## [1325,] 10.600000 1
## [1326,] 10.600000 1
## [1327,] 10.600000 1
## [1328,] 10.600000 1
## [1329,] 9.300000 0
## [1330,] 9.800000 1
## [1331,] 9.800000 1
## [1332,] 9.200000 0
## [1333,] 9.700000 1
## [1334,] 9.600000 0
## [1335,] 10.000000 0
## [1336,] 12.600000 1
## [1337,] 9.500000 0
## [1338,] 9.500000 0
## [1339,] 9.500000 0
## [1340,] 10.500000 1
## [1341,] 10.500000 1
## [1342,] 10.500000 1
## [1343,] 10.400000 1
## [1344,] 10.500000 1
## [1345,] 11.000000 0
## [1346,] 10.200000 1
## [1347,] 11.400000 0
## [1348,] 9.500000 0
## [1349,] 9.500000 0
## [1350,] 10.800000 0
## [1351,] 10.000000 0
## [1352,] 11.800000 1
## [1353,] 10.300000 0
## [1354,] 10.300000 0
## [1355,] 10.000000 0
## [1356,] 10.100000 0
## [1357,] 10.100000 0
## [1358,] 11.100000 1
## [1359,] 9.500000 0
## [1360,] 10.033333 1
## [1361,] 10.900000 0
## [1362,] 10.100000 0
## [1363,] 10.033333 1
## [1364,] 9.800000 0
## [1365,] 11.800000 1
## [1366,] 9.500000 0
## [1367,] 9.800000 0
## [1368,] 10.500000 1
## [1369,] 9.800000 1
## [1370,] 10.400000 0
## [1371,] 9.200000 0
## [1372,] 11.600000 1
## [1373,] 9.200000 0
## [1374,] 9.300000 0
## [1375,] 9.800000 0
## [1376,] 9.900000 0
## [1377,] 10.000000 0
## [1378,] 12.200000 1
## [1379,] 9.900000 1
## [1380,] 10.800000 1
## [1381,] 10.800000 1
## [1382,] 9.600000 0
## [1383,] 9.900000 0
## [1384,] 9.900000 0
## [1385,] 9.500000 0
## [1386,] 9.200000 0
## [1387,] 9.600000 0
## [1388,] 9.600000 0
## [1389,] 10.300000 0
## [1390,] 9.700000 0
## [1391,] 12.500000 1
## [1392,] 11.000000 0
## [1393,] 9.800000 0
## [1394,] 10.200000 0
## [1395,] 9.200000 0
## [1396,] 9.550000 1
## [1397,] 9.550000 0
## [1398,] 9.900000 0
## [1399,] 10.200000 1
## [1400,] 11.100000 1
## [1401,] 9.900000 0
## [1402,] 9.900000 0
## [1403,] 11.900000 1
## [1404,] 10.000000 1
## [1405,] 10.700000 1
## [1406,] 11.300000 1
## [1407,] 10.900000 1
## [1408,] 10.700000 1
## [1409,] 12.400000 1
## [1410,] 10.700000 1
## [1411,] 11.900000 1
## [1412,] 10.600000 1
## [1413,] 10.900000 1
## [1414,] 10.100000 0
## [1415,] 9.600000 0
## [1416,] 9.400000 0
## [1417,] 9.600000 0
## [1418,] 12.100000 1
## [1419,] 9.800000 0
## [1420,] 9.900000 0
## [1421,] 9.800000 0
## [1422,] 9.400000 0
## [1423,] 11.400000 1
## [1424,] 11.000000 0
## [1425,] 9.600000 1
## [1426,] 9.600000 1
## [1427,] 12.100000 1
## [1428,] 11.200000 0
## [1429,] 11.000000 0
## [1430,] 11.300000 0
## [1431,] 10.400000 0
## [1432,] 9.900000 1
## [1433,] 11.700000 1
## [1434,] 10.100000 1
## [1435,] 9.000000 1
## [1436,] 9.000000 1
## [1437,] 8.500000 0
## [1438,] 11.100000 0
## [1439,] 10.300000 0
## [1440,] 11.066667 1
## [1441,] 11.300000 1
## [1442,] 9.566667 1
## [1443,] 9.800000 0
## [1444,] 11.700000 0
## [1445,] 11.100000 1
## [1446,] 9.600000 1
## [1447,] 9.800000 0
## [1448,] 9.700000 0
## [1449,] 9.700000 0
## [1450,] 11.300000 1
## [1451,] 11.300000 1
## [1452,] 11.000000 1
## [1453,] 11.600000 1
## [1454,] 9.000000 0
## [1455,] 10.900000 1
## [1456,] 10.900000 1
## [1457,] 10.550000 1
## [1458,] 9.000000 0
## [1459,] 10.500000 0
## [1460,] 11.900000 1
## [1461,] 10.400000 1
## [1462,] 10.000000 0
## [1463,] 10.400000 1
## [1464,] 10.800000 1
## [1465,] 9.700000 0
## [1466,] 9.700000 0
## [1467,] 10.000000 1
## [1468,] 11.000000 0
## [1469,] 10.000000 1
## [1470,] 9.700000 0
## [1471,] 9.700000 0
## [1472,] 12.600000 0
## [1473,] 11.100000 1
## [1474,] 11.000000 0
## [1475,] 8.800000 0
## [1476,] 13.566667 1
## [1477,] 8.800000 0
## [1478,] 13.600000 1
## [1479,] 10.200000 0
## [1480,] 10.600000 0
## [1481,] 10.100000 0
## [1482,] 10.600000 0
## [1483,] 10.300000 0
## [1484,] 10.700000 0
## [1485,] 10.900000 0
## [1486,] 9.700000 0
## [1487,] 10.300000 0
## [1488,] 10.600000 0
## [1489,] 11.400000 0
## [1490,] 10.800000 1
## [1491,] 12.400000 1
## [1492,] 11.400000 0
## [1493,] 11.950000 0
## [1494,] 9.700000 0
## [1495,] 10.000000 1
## [1496,] 10.600000 1
## [1497,] 9.700000 0
## [1498,] 11.500000 1
## [1499,] 10.800000 1
## [1500,] 11.500000 1
## [1501,] 9.600000 0
## [1502,] 9.400000 0
## [1503,] 9.800000 0
## [1504,] 11.000000 1
## [1505,] 11.400000 1
## [1506,] 9.950000 0
## [1507,] 10.800000 1
## [1508,] 11.400000 1
## [1509,] 10.600000 1
## [1510,] 11.100000 0
## [1511,] 9.700000 1
## [1512,] 9.800000 0
## [1513,] 10.400000 1
## [1514,] 10.500000 1
## [1515,] 9.233333 1
## [1516,] 9.250000 1
## [1517,] 10.600000 0
## [1518,] 10.300000 1
## [1519,] 10.500000 0
## [1520,] 10.200000 0
## [1521,] 10.300000 1
## [1522,] 9.050000 0
## [1523,] 10.600000 0
## [1524,] 10.400000 0
## [1525,] 10.000000 1
## [1526,] 9.700000 0
## [1527,] 9.600000 1
## [1528,] 10.800000 1
## [1529,] 10.100000 1
## [1530,] 10.200000 1
## [1531,] 11.300000 1
## [1532,] 10.200000 0
## [1533,] 9.900000 1
## [1534,] 9.000000 0
## [1535,] 11.700000 1
## [1536,] 9.700000 1
## [1537,] 10.300000 1
## [1538,] 10.200000 1
## [1539,] 12.000000 0
## [1540,] 9.900000 0
## [1541,] 11.600000 1
## [1542,] 10.900000 1
## [1543,] 10.750000 1
## [1544,] 10.400000 1
## [1545,] 11.200000 1
## [1546,] 10.100000 1
## [1547,] 10.200000 0
## [1548,] 12.100000 0
## [1549,] 10.400000 0
## [1550,] 11.400000 1
## [1551,] 9.500000 0
## [1552,] 9.400000 0
## [1553,] 11.300000 1
## [1554,] 9.400000 0
## [1555,] 11.000000 1
## [1556,] 10.550000 1
## [1557,] 9.900000 0
## [1558,] 11.000000 1
## [1559,] 9.500000 0
## [1560,] 9.900000 0
## [1561,] 9.900000 0
## [1562,] 9.900000 0
## [1563,] 10.100000 0
## [1564,] 10.100000 0
## [1565,] 10.100000 0
## [1566,] 10.900000 1
## [1567,] 11.200000 1
## [1568,] 10.100000 0
## [1569,] 9.200000 0
## [1570,] 11.500000 1
## [1571,] 12.400000 1
## [1572,] 11.100000 1
## [1573,] 9.500000 0
## [1574,] 12.500000 1
## [1575,] 10.500000 1
## [1576,] 11.800000 1
## [1577,] 10.800000 1
## [1578,] 11.900000 1
## [1579,] 11.300000 1
## [1580,] 11.300000 0
## [1581,] 11.900000 1
## [1582,] 11.300000 0
## [1583,] 11.900000 0
## [1584,] 9.800000 0
## [1585,] 11.600000 1
## [1586,] 11.500000 1
## [1587,] 11.400000 1
## [1588,] 10.900000 1
## [1589,] 12.800000 1
## [1590,] 9.200000 0
## [1591,] 11.600000 1
## [1592,] 11.600000 1
## [1593,] 11.000000 1
## [1594,] 9.500000 1
## [1595,] 10.500000 0
## [1596,] 11.200000 1
## [1597,] 11.000000 1
## [1598,] 10.200000 0
## [1599,] 11.000000 1colnames(df_new)[12] <- "quality"
df_new## fixed acidity volatile acidity citric acid residual sugar chlorides
## [1,] 7.4 0.700 0.00 1.90 0.076
## [2,] 7.8 0.880 0.00 2.60 0.098
## [3,] 7.8 0.760 0.04 2.30 0.092
## [4,] 11.2 0.280 0.56 1.90 0.075
## [5,] 7.4 0.700 0.00 1.90 0.076
## [6,] 7.4 0.660 0.00 1.80 0.075
## [7,] 7.9 0.600 0.06 1.60 0.069
## [8,] 7.3 0.650 0.00 1.20 0.065
## [9,] 7.8 0.580 0.02 2.00 0.073
## [10,] 7.5 0.500 0.36 6.10 0.071
## [11,] 6.7 0.580 0.08 1.80 0.097
## [12,] 7.5 0.500 0.36 6.10 0.071
## [13,] 5.6 0.615 0.00 1.60 0.089
## [14,] 7.8 0.610 0.29 1.60 0.114
## [15,] 8.9 0.620 0.18 3.80 0.176
## [16,] 8.9 0.620 0.19 3.90 0.170
## [17,] 8.5 0.280 0.56 1.80 0.092
## [18,] 8.1 0.560 0.28 1.70 0.368
## [19,] 7.4 0.590 0.08 4.40 0.086
## [20,] 7.9 0.320 0.51 1.80 0.341
## [21,] 8.9 0.220 0.48 1.80 0.077
## [22,] 7.6 0.390 0.31 2.30 0.082
## [23,] 7.9 0.430 0.21 1.60 0.106
## [24,] 8.5 0.490 0.11 2.30 0.084
## [25,] 6.9 0.400 0.14 2.40 0.085
## [26,] 6.3 0.390 0.16 1.40 0.080
## [27,] 7.6 0.410 0.24 1.80 0.080
## [28,] 7.9 0.430 0.21 1.60 0.106
## [29,] 7.1 0.710 0.00 1.90 0.080
## [30,] 7.8 0.645 0.00 2.00 0.082
## [31,] 6.7 0.675 0.07 2.40 0.089
## [32,] 6.9 0.685 0.00 2.50 0.105
## [33,] 8.3 0.655 0.12 2.30 0.083
## [34,] 6.9 0.605 0.12 10.70 0.073
## [35,] 5.2 0.320 0.25 1.80 0.103
## [36,] 7.8 0.645 0.00 5.50 0.086
## [37,] 7.8 0.600 0.14 2.40 0.086
## [38,] 8.1 0.380 0.28 2.10 0.066
## [39,] 5.7 1.130 0.09 1.50 0.172
## [40,] 7.3 0.450 0.36 5.90 0.074
## [41,] 7.3 0.450 0.36 5.90 0.074
## [42,] 8.8 0.610 0.30 2.80 0.088
## [43,] 7.5 0.490 0.20 2.60 0.332
## [44,] 8.1 0.660 0.22 2.20 0.069
## [45,] 6.8 0.670 0.02 1.80 0.050
## [46,] 4.6 0.520 0.15 2.10 0.054
## [47,] 7.7 0.935 0.43 2.20 0.114
## [48,] 8.7 0.290 0.52 1.60 0.113
## [49,] 6.4 0.400 0.23 1.60 0.066
## [50,] 5.6 0.310 0.37 1.40 0.074
## [51,] 8.8 0.660 0.26 1.70 0.074
## [52,] 6.6 0.520 0.04 2.20 0.069
## [53,] 6.6 0.500 0.04 2.10 0.068
## [54,] 8.6 0.380 0.36 3.00 0.081
## [55,] 7.6 0.510 0.15 2.80 0.110
## [56,] 7.7 0.620 0.04 3.80 0.084
## [57,] 10.2 0.420 0.57 3.40 0.070
## [58,] 7.5 0.630 0.12 5.10 0.111
## [59,] 7.8 0.590 0.18 2.30 0.076
## [60,] 7.3 0.390 0.31 2.40 0.074
## [61,] 8.8 0.400 0.40 2.20 0.079
## [62,] 7.7 0.690 0.49 1.80 0.115
## [63,] 7.5 0.520 0.16 1.90 0.085
## [64,] 7.0 0.735 0.05 2.00 0.081
## [65,] 7.2 0.725 0.05 4.65 0.086
## [66,] 7.2 0.725 0.05 4.65 0.086
## [67,] 7.5 0.520 0.11 1.50 0.079
## [68,] 6.6 0.705 0.07 1.60 0.076
## [69,] 9.3 0.320 0.57 2.00 0.074
## [70,] 8.0 0.705 0.05 1.90 0.074
## [71,] 7.7 0.630 0.08 1.90 0.076
## [72,] 7.7 0.670 0.23 2.10 0.088
## [73,] 7.7 0.690 0.22 1.90 0.084
## [74,] 8.3 0.675 0.26 2.10 0.084
## [75,] 9.7 0.320 0.54 2.50 0.094
## [76,] 8.8 0.410 0.64 2.20 0.093
## [77,] 8.8 0.410 0.64 2.20 0.093
## [78,] 6.8 0.785 0.00 2.40 0.104
## [79,] 6.7 0.750 0.12 2.00 0.086
## [80,] 8.3 0.625 0.20 1.50 0.080
## [81,] 6.2 0.450 0.20 1.60 0.069
## [82,] 7.8 0.430 0.70 1.90 0.464
## [83,] 7.4 0.500 0.47 2.00 0.086
## [84,] 7.3 0.670 0.26 1.80 0.401
## [85,] 6.3 0.300 0.48 1.80 0.069
## [86,] 6.9 0.550 0.15 2.20 0.076
## [87,] 8.6 0.490 0.28 1.90 0.110
## [88,] 7.7 0.490 0.26 1.90 0.062
## [89,] 9.3 0.390 0.44 2.10 0.107
## [90,] 7.0 0.620 0.08 1.80 0.076
## [91,] 7.9 0.520 0.26 1.90 0.079
## [92,] 8.6 0.490 0.28 1.90 0.110
## [93,] 8.6 0.490 0.29 2.00 0.110
## [94,] 7.7 0.490 0.26 1.90 0.062
## [95,] 5.0 1.020 0.04 1.40 0.045
## [96,] 4.7 0.600 0.17 2.30 0.058
## [97,] 6.8 0.775 0.00 3.00 0.102
## [98,] 7.0 0.500 0.25 2.00 0.070
## [99,] 7.6 0.900 0.06 2.50 0.079
## [100,] 8.1 0.545 0.18 1.90 0.080
## [101,] 8.3 0.610 0.30 2.10 0.084
## [102,] 7.8 0.500 0.30 1.90 0.075
## [103,] 8.1 0.545 0.18 1.90 0.080
## [104,] 8.1 0.575 0.22 2.10 0.077
## [105,] 7.2 0.490 0.24 2.20 0.070
## [106,] 8.1 0.575 0.22 2.10 0.077
## [107,] 7.8 0.410 0.68 1.70 0.467
## [108,] 6.2 0.630 0.31 1.70 0.088
## [109,] 8.0 0.330 0.53 2.50 0.091
## [110,] 8.1 0.785 0.52 2.00 0.122
## [111,] 7.8 0.560 0.19 1.80 0.104
## [112,] 8.4 0.620 0.09 2.20 0.084
## [113,] 8.4 0.600 0.10 2.20 0.085
## [114,] 10.1 0.310 0.44 2.30 0.080
## [115,] 7.8 0.560 0.19 1.80 0.104
## [116,] 9.4 0.400 0.31 2.20 0.090
## [117,] 8.3 0.540 0.28 1.90 0.077
## [118,] 7.8 0.560 0.12 2.00 0.082
## [119,] 8.8 0.550 0.04 2.20 0.119
## [120,] 7.0 0.690 0.08 1.80 0.097
## [121,] 7.3 1.070 0.09 1.70 0.178
## [122,] 8.8 0.550 0.04 2.20 0.119
## [123,] 7.3 0.695 0.00 2.50 0.075
## [124,] 8.0 0.710 0.00 2.60 0.080
## [125,] 7.8 0.500 0.17 1.60 0.082
## [126,] 9.0 0.620 0.04 1.90 0.146
## [127,] 8.2 1.330 0.00 1.70 0.081
## [128,] 8.1 1.330 0.00 1.80 0.082
## [129,] 8.0 0.590 0.16 1.80 0.065
## [130,] 6.1 0.380 0.15 1.80 0.072
## [131,] 8.0 0.745 0.56 2.00 0.118
## [132,] 5.6 0.500 0.09 2.30 0.049
## [133,] 5.6 0.500 0.09 2.30 0.049
## [134,] 6.6 0.500 0.01 1.50 0.060
## [135,] 7.9 1.040 0.05 2.20 0.084
## [136,] 8.4 0.745 0.11 1.90 0.090
## [137,] 8.3 0.715 0.15 1.80 0.089
## [138,] 7.2 0.415 0.36 2.00 0.081
## [139,] 7.8 0.560 0.19 2.10 0.081
## [140,] 7.8 0.560 0.19 2.00 0.081
## [141,] 8.4 0.745 0.11 1.90 0.090
## [142,] 8.3 0.715 0.15 1.80 0.089
## [143,] 5.2 0.340 0.00 1.80 0.050
## [144,] 6.3 0.390 0.08 1.70 0.066
## [145,] 5.2 0.340 0.00 1.80 0.050
## [146,] 8.1 0.670 0.55 1.80 0.117
## [147,] 5.8 0.680 0.02 1.80 0.087
## [148,] 7.6 0.490 0.26 1.60 0.236
## [149,] 6.9 0.490 0.10 2.30 0.074
## [150,] 8.2 0.400 0.44 2.80 0.089
## [151,] 7.3 0.330 0.47 2.10 0.077
## [152,] 9.2 0.520 1.00 3.40 0.610
## [153,] 7.5 0.600 0.03 1.80 0.095
## [154,] 7.5 0.600 0.03 1.80 0.095
## [155,] 7.1 0.430 0.42 5.50 0.070
## [156,] 7.1 0.430 0.42 5.50 0.071
## [157,] 7.1 0.430 0.42 5.50 0.070
## [158,] 7.1 0.430 0.42 5.50 0.071
## [159,] 7.1 0.680 0.00 2.20 0.073
## [160,] 6.8 0.600 0.18 1.90 0.079
## [161,] 7.6 0.950 0.03 2.00 0.090
## [162,] 7.6 0.680 0.02 1.30 0.072
## [163,] 7.8 0.530 0.04 1.70 0.076
## [164,] 7.4 0.600 0.26 7.30 0.070
## [165,] 7.3 0.590 0.26 7.20 0.070
## [166,] 7.8 0.630 0.48 1.70 0.100
## [167,] 6.8 0.640 0.10 2.10 0.085
## [168,] 7.3 0.550 0.03 1.60 0.072
## [169,] 6.8 0.630 0.07 2.10 0.089
## [170,] 7.5 0.705 0.24 1.80 0.360
## [171,] 7.9 0.885 0.03 1.80 0.058
## [172,] 8.0 0.420 0.17 2.00 0.073
## [173,] 8.0 0.420 0.17 2.00 0.073
## [174,] 7.4 0.620 0.05 1.90 0.068
## [175,] 7.3 0.380 0.21 2.00 0.080
## [176,] 6.9 0.500 0.04 1.50 0.085
## [177,] 7.3 0.380 0.21 2.00 0.080
## [178,] 7.5 0.520 0.42 2.30 0.087
## [179,] 7.0 0.805 0.00 2.50 0.068
## [180,] 8.8 0.610 0.14 2.40 0.067
## [181,] 8.8 0.610 0.14 2.40 0.067
## [182,] 8.9 0.610 0.49 2.00 0.270
## [183,] 7.2 0.730 0.02 2.50 0.076
## [184,] 6.8 0.610 0.20 1.80 0.077
## [185,] 6.7 0.620 0.21 1.90 0.079
## [186,] 8.9 0.310 0.57 2.00 0.111
## [187,] 7.4 0.390 0.48 2.00 0.082
## [188,] 7.7 0.705 0.10 2.60 0.084
## [189,] 7.9 0.500 0.33 2.00 0.084
## [190,] 7.9 0.490 0.32 1.90 0.082
## [191,] 8.2 0.500 0.35 2.90 0.077
## [192,] 6.4 0.370 0.25 1.90 0.074
## [193,] 6.8 0.630 0.12 3.80 0.099
## [194,] 7.6 0.550 0.21 2.20 0.071
## [195,] 7.6 0.550 0.21 2.20 0.071
## [196,] 7.8 0.590 0.33 2.00 0.074
## [197,] 7.3 0.580 0.30 2.40 0.074
## [198,] 11.5 0.300 0.60 2.00 0.067
## [199,] 5.4 0.835 0.08 1.20 0.046
## [200,] 6.9 1.090 0.06 2.10 0.061
## [201,] 9.6 0.320 0.47 1.40 0.056
## [202,] 8.8 0.370 0.48 2.10 0.097
## [203,] 6.8 0.500 0.11 1.50 0.075
## [204,] 7.0 0.420 0.35 1.60 0.088
## [205,] 7.0 0.430 0.36 1.60 0.089
## [206,] 12.8 0.300 0.74 2.60 0.095
## [207,] 12.8 0.300 0.74 2.60 0.095
## [208,] 7.8 0.570 0.31 1.80 0.069
## [209,] 7.8 0.440 0.28 2.70 0.100
## [210,] 11.0 0.300 0.58 2.10 0.054
## [211,] 9.7 0.530 0.60 2.00 0.039
## [212,] 8.0 0.725 0.24 2.80 0.083
## [213,] 11.6 0.440 0.64 2.10 0.059
## [214,] 8.2 0.570 0.26 2.20 0.060
## [215,] 7.8 0.735 0.08 2.40 0.092
## [216,] 7.0 0.490 0.49 5.60 0.060
## [217,] 8.7 0.625 0.16 2.00 0.101
## [218,] 8.1 0.725 0.22 2.20 0.072
## [219,] 7.5 0.490 0.19 1.90 0.076
## [220,] 7.8 0.530 0.33 2.40 0.080
## [221,] 7.8 0.340 0.37 2.00 0.082
## [222,] 7.4 0.530 0.26 2.00 0.101
## [223,] 6.8 0.610 0.04 1.50 0.057
## [224,] 8.6 0.645 0.25 2.00 0.083
## [225,] 8.4 0.635 0.36 2.00 0.089
## [226,] 7.7 0.430 0.25 2.60 0.073
## [227,] 8.9 0.590 0.50 2.00 0.337
## [228,] 9.0 0.820 0.14 2.60 0.089
## [229,] 7.7 0.430 0.25 2.60 0.073
## [230,] 6.9 0.520 0.25 2.60 0.081
## [231,] 5.2 0.480 0.04 1.60 0.054
## [232,] 8.0 0.380 0.06 1.80 0.078
## [233,] 8.5 0.370 0.20 2.80 0.090
## [234,] 6.9 0.520 0.25 2.60 0.081
## [235,] 8.2 1.000 0.09 2.30 0.065
## [236,] 7.2 0.630 0.00 1.90 0.097
## [237,] 7.2 0.630 0.00 1.90 0.097
## [238,] 7.2 0.645 0.00 1.90 0.097
## [239,] 7.2 0.630 0.00 1.90 0.097
## [240,] 8.2 1.000 0.09 2.30 0.065
## [241,] 8.9 0.635 0.37 1.70 0.263
## [242,] 12.0 0.380 0.56 2.10 0.093
## [243,] 7.7 0.580 0.10 1.80 0.102
## [244,] 15.0 0.210 0.44 2.20 0.075
## [245,] 15.0 0.210 0.44 2.20 0.075
## [246,] 7.3 0.660 0.00 2.00 0.084
## [247,] 7.1 0.680 0.07 1.90 0.075
## [248,] 8.2 0.600 0.17 2.30 0.072
## [249,] 7.7 0.530 0.06 1.70 0.074
## [250,] 7.3 0.660 0.00 2.00 0.084
## [251,] 10.8 0.320 0.44 1.60 0.063
## [252,] 7.1 0.600 0.00 1.80 0.074
## [253,] 11.1 0.350 0.48 3.10 0.090
## [254,] 7.7 0.775 0.42 1.90 0.092
## [255,] 7.1 0.600 0.00 1.80 0.074
## [256,] 8.0 0.570 0.23 3.20 0.073
## [257,] 9.4 0.340 0.37 2.20 0.075
## [258,] 6.6 0.695 0.00 2.10 0.075
## [259,] 7.7 0.410 0.76 1.80 0.611
## [260,] 10.0 0.310 0.47 2.60 0.085
## [261,] 7.9 0.330 0.23 1.70 0.077
## [262,] 7.0 0.975 0.04 2.00 0.087
## [263,] 8.0 0.520 0.03 1.70 0.070
## [264,] 7.9 0.370 0.23 1.80 0.077
## [265,] 12.5 0.560 0.49 2.40 0.064
## [266,] 11.8 0.260 0.52 1.80 0.071
## [267,] 8.1 0.870 0.00 3.30 0.096
## [268,] 7.9 0.350 0.46 3.60 0.078
## [269,] 6.9 0.540 0.04 3.00 0.077
## [270,] 11.5 0.180 0.51 4.00 0.104
## [271,] 7.9 0.545 0.06 4.00 0.087
## [272,] 11.5 0.180 0.51 4.00 0.104
## [273,] 10.9 0.370 0.58 4.00 0.071
## [274,] 8.4 0.715 0.20 2.40 0.076
## [275,] 7.5 0.650 0.18 7.00 0.088
## [276,] 7.9 0.545 0.06 4.00 0.087
## [277,] 6.9 0.540 0.04 3.00 0.077
## [278,] 11.5 0.180 0.51 4.00 0.104
## [279,] 10.3 0.320 0.45 6.40 0.073
## [280,] 8.9 0.400 0.32 5.60 0.087
## [281,] 11.4 0.260 0.44 3.60 0.071
## [282,] 7.7 0.270 0.68 3.50 0.358
## [283,] 7.6 0.520 0.12 3.00 0.067
## [284,] 8.9 0.400 0.32 5.60 0.087
## [285,] 9.9 0.590 0.07 3.40 0.102
## [286,] 9.9 0.590 0.07 3.40 0.102
## [287,] 12.0 0.450 0.55 2.00 0.073
## [288,] 7.5 0.400 0.12 3.00 0.092
## [289,] 8.7 0.520 0.09 2.50 0.091
## [290,] 11.6 0.420 0.53 3.30 0.105
## [291,] 8.7 0.520 0.09 2.50 0.091
## [292,] 11.0 0.200 0.48 2.00 0.343
## [293,] 10.4 0.550 0.23 2.70 0.091
## [294,] 6.9 0.360 0.25 2.40 0.098
## [295,] 13.3 0.340 0.52 3.20 0.094
## [296,] 10.8 0.500 0.46 2.50 0.073
## [297,] 10.6 0.830 0.37 2.60 0.086
## [298,] 7.1 0.630 0.06 2.00 0.083
## [299,] 7.2 0.650 0.02 2.30 0.094
## [300,] 6.9 0.670 0.06 2.10 0.080
## [301,] 7.5 0.530 0.06 2.60 0.086
## [302,] 11.1 0.180 0.48 1.50 0.068
## [303,] 8.3 0.705 0.12 2.60 0.092
## [304,] 7.4 0.670 0.12 1.60 0.186
## [305,] 8.4 0.650 0.60 2.10 0.112
## [306,] 10.3 0.530 0.48 2.50 0.063
## [307,] 7.6 0.620 0.32 2.20 0.082
## [308,] 10.3 0.410 0.42 2.40 0.213
## [309,] 10.3 0.430 0.44 2.40 0.214
## [310,] 7.4 0.290 0.38 1.70 0.062
## [311,] 10.3 0.530 0.48 2.50 0.063
## [312,] 7.9 0.530 0.24 2.00 0.072
## [313,] 9.0 0.460 0.31 2.80 0.093
## [314,] 8.6 0.470 0.30 3.00 0.076
## [315,] 7.4 0.360 0.29 2.60 0.087
## [316,] 7.1 0.350 0.29 2.50 0.096
## [317,] 9.6 0.560 0.23 3.40 0.102
## [318,] 9.6 0.770 0.12 2.90 0.082
## [319,] 9.8 0.660 0.39 3.20 0.083
## [320,] 9.6 0.770 0.12 2.90 0.082
## [321,] 9.8 0.660 0.39 3.20 0.083
## [322,] 9.3 0.610 0.26 3.40 0.090
## [323,] 7.8 0.620 0.05 2.30 0.079
## [324,] 10.3 0.590 0.42 2.80 0.090
## [325,] 10.0 0.490 0.20 11.00 0.071
## [326,] 10.0 0.490 0.20 11.00 0.071
## [327,] 11.6 0.530 0.66 3.65 0.121
## [328,] 10.3 0.440 0.50 4.50 0.107
## [329,] 13.4 0.270 0.62 2.60 0.082
## [330,] 10.7 0.460 0.39 2.00 0.061
## [331,] 10.2 0.360 0.64 2.90 0.122
## [332,] 10.2 0.360 0.64 2.90 0.122
## [333,] 8.0 0.580 0.28 3.20 0.066
## [334,] 8.4 0.560 0.08 2.10 0.105
## [335,] 7.9 0.650 0.01 2.50 0.078
## [336,] 11.9 0.695 0.53 3.40 0.128
## [337,] 8.9 0.430 0.45 1.90 0.052
## [338,] 7.8 0.430 0.32 2.80 0.080
## [339,] 12.4 0.490 0.58 3.00 0.103
## [340,] 12.5 0.280 0.54 2.30 0.082
## [341,] 12.2 0.340 0.50 2.40 0.066
## [342,] 10.6 0.420 0.48 2.70 0.065
## [343,] 10.9 0.390 0.47 1.80 0.118
## [344,] 10.9 0.390 0.47 1.80 0.118
## [345,] 11.9 0.570 0.50 2.60 0.082
## [346,] 7.0 0.685 0.00 1.90 0.067
## [347,] 6.6 0.815 0.02 2.70 0.072
## [348,] 13.8 0.490 0.67 3.00 0.093
## [349,] 9.6 0.560 0.31 2.80 0.089
## [350,] 9.1 0.785 0.00 2.60 0.093
## [351,] 10.7 0.670 0.22 2.70 0.107
## [352,] 9.1 0.795 0.00 2.60 0.096
## [353,] 7.7 0.665 0.00 2.40 0.090
## [354,] 13.5 0.530 0.79 4.80 0.120
## [355,] 6.1 0.210 0.40 1.40 0.066
## [356,] 6.7 0.750 0.01 2.40 0.078
## [357,] 11.5 0.410 0.52 3.00 0.080
## [358,] 10.5 0.420 0.66 2.95 0.116
## [359,] 11.9 0.430 0.66 3.10 0.109
## [360,] 12.6 0.380 0.66 2.60 0.088
## [361,] 8.2 0.700 0.23 2.00 0.099
## [362,] 8.6 0.450 0.31 2.60 0.086
## [363,] 11.9 0.580 0.66 2.50 0.072
## [364,] 12.5 0.460 0.63 2.00 0.071
## [365,] 12.8 0.615 0.66 5.80 0.083
## [366,] 10.0 0.420 0.50 3.40 0.107
## [367,] 12.8 0.615 0.66 5.80 0.083
## [368,] 10.4 0.575 0.61 2.60 0.076
## [369,] 10.3 0.340 0.52 2.80 0.159
## [370,] 9.4 0.270 0.53 2.40 0.074
## [371,] 6.9 0.765 0.02 2.30 0.063
## [372,] 7.9 0.240 0.40 1.60 0.056
## [373,] 9.1 0.280 0.48 1.80 0.067
## [374,] 7.4 0.550 0.22 2.20 0.106
## [375,] 14.0 0.410 0.63 3.80 0.089
## [376,] 11.5 0.540 0.71 4.40 0.124
## [377,] 11.5 0.450 0.50 3.00 0.078
## [378,] 9.4 0.270 0.53 2.40 0.074
## [379,] 11.4 0.625 0.66 6.20 0.088
## [380,] 8.3 0.420 0.38 2.50 0.094
## [381,] 8.3 0.260 0.42 2.00 0.080
## [382,] 13.7 0.415 0.68 2.90 0.085
## [383,] 8.3 0.260 0.42 2.00 0.080
## [384,] 8.3 0.260 0.42 2.00 0.080
## [385,] 7.7 0.510 0.28 2.10 0.087
## [386,] 7.4 0.630 0.07 2.40 0.090
## [387,] 7.8 0.540 0.26 2.00 0.088
## [388,] 8.3 0.660 0.15 1.90 0.079
## [389,] 7.8 0.460 0.26 1.90 0.088
## [390,] 9.6 0.380 0.31 2.50 0.096
## [391,] 5.6 0.850 0.05 1.40 0.045
## [392,] 13.7 0.415 0.68 2.90 0.085
## [393,] 9.5 0.370 0.52 2.00 0.082
## [394,] 8.4 0.665 0.61 2.00 0.112
## [395,] 12.7 0.600 0.65 2.30 0.063
## [396,] 12.0 0.370 0.76 4.20 0.066
## [397,] 6.6 0.735 0.02 7.90 0.122
## [398,] 11.5 0.590 0.59 2.60 0.087
## [399,] 11.5 0.590 0.59 2.60 0.087
## [400,] 8.7 0.765 0.22 2.30 0.064
## [401,] 6.6 0.735 0.02 7.90 0.122
## [402,] 7.7 0.260 0.30 1.70 0.059
## [403,] 12.2 0.480 0.54 2.60 0.085
## [404,] 11.4 0.600 0.49 2.70 0.085
## [405,] 7.7 0.690 0.05 2.70 0.075
## [406,] 8.7 0.310 0.46 1.40 0.059
## [407,] 9.8 0.440 0.47 2.50 0.063
## [408,] 12.0 0.390 0.66 3.00 0.093
## [409,] 10.4 0.340 0.58 3.70 0.174
## [410,] 12.5 0.460 0.49 4.50 0.070
## [411,] 9.0 0.430 0.34 2.50 0.080
## [412,] 9.1 0.450 0.35 2.40 0.080
## [413,] 7.1 0.735 0.16 1.90 0.100
## [414,] 9.9 0.400 0.53 6.70 0.097
## [415,] 8.8 0.520 0.34 2.70 0.087
## [416,] 8.6 0.725 0.24 6.60 0.117
## [417,] 10.6 0.480 0.64 2.20 0.111
## [418,] 7.0 0.580 0.12 1.90 0.091
## [419,] 11.9 0.380 0.51 2.00 0.121
## [420,] 6.8 0.770 0.00 1.80 0.066
## [421,] 9.5 0.560 0.33 2.40 0.089
## [422,] 6.6 0.840 0.03 2.30 0.059
## [423,] 7.7 0.960 0.20 2.00 0.047
## [424,] 10.5 0.240 0.47 2.10 0.066
## [425,] 7.7 0.960 0.20 2.00 0.047
## [426,] 6.6 0.840 0.03 2.30 0.059
## [427,] 6.4 0.670 0.08 2.10 0.045
## [428,] 9.5 0.780 0.22 1.90 0.077
## [429,] 9.1 0.520 0.33 1.30 0.070
## [430,] 12.8 0.840 0.63 2.40 0.088
## [431,] 10.5 0.240 0.47 2.10 0.066
## [432,] 7.8 0.550 0.35 2.20 0.074
## [433,] 11.9 0.370 0.69 2.30 0.078
## [434,] 12.3 0.390 0.63 2.30 0.091
## [435,] 10.4 0.410 0.55 3.20 0.076
## [436,] 12.3 0.390 0.63 2.30 0.091
## [437,] 8.0 0.670 0.30 2.00 0.060
## [438,] 11.1 0.450 0.73 3.20 0.066
## [439,] 10.4 0.410 0.55 3.20 0.076
## [440,] 7.0 0.620 0.18 1.50 0.062
## [441,] 12.6 0.310 0.72 2.20 0.072
## [442,] 11.9 0.400 0.65 2.15 0.068
## [443,] 15.6 0.685 0.76 3.70 0.100
## [444,] 10.0 0.440 0.49 2.70 0.077
## [445,] 5.3 0.570 0.01 1.70 0.054
## [446,] 9.5 0.735 0.10 2.10 0.079
## [447,] 12.5 0.380 0.60 2.60 0.081
## [448,] 9.3 0.480 0.29 2.10 0.127
## [449,] 8.6 0.530 0.22 2.00 0.100
## [450,] 11.9 0.390 0.69 2.80 0.095
## [451,] 11.9 0.390 0.69 2.80 0.095
## [452,] 8.4 0.370 0.53 1.80 0.413
## [453,] 6.8 0.560 0.03 1.70 0.084
## [454,] 10.4 0.330 0.63 2.80 0.084
## [455,] 7.0 0.230 0.40 1.60 0.063
## [456,] 11.3 0.620 0.67 5.20 0.086
## [457,] 8.9 0.590 0.39 2.30 0.095
## [458,] 9.2 0.630 0.21 2.70 0.097
## [459,] 10.4 0.330 0.63 2.80 0.084
## [460,] 11.6 0.580 0.66 2.20 0.074
## [461,] 9.2 0.430 0.52 2.30 0.083
## [462,] 8.3 0.615 0.22 2.60 0.087
## [463,] 11.0 0.260 0.68 2.55 0.085
## [464,] 8.1 0.660 0.70 2.20 0.098
## [465,] 11.5 0.315 0.54 2.10 0.084
## [466,] 10.0 0.290 0.40 2.90 0.098
## [467,] 10.3 0.500 0.42 2.00 0.069
## [468,] 8.8 0.460 0.45 2.60 0.065
## [469,] 11.4 0.360 0.69 2.10 0.090
## [470,] 8.7 0.820 0.02 1.20 0.070
## [471,] 13.0 0.320 0.65 2.60 0.093
## [472,] 9.6 0.540 0.42 2.40 0.081
## [473,] 12.5 0.370 0.55 2.60 0.083
## [474,] 9.9 0.350 0.55 2.10 0.062
## [475,] 10.5 0.280 0.51 1.70 0.080
## [476,] 9.6 0.680 0.24 2.20 0.087
## [477,] 9.3 0.270 0.41 2.00 0.091
## [478,] 10.4 0.240 0.49 1.80 0.075
## [479,] 9.6 0.680 0.24 2.20 0.087
## [480,] 9.4 0.685 0.11 2.70 0.077
## [481,] 10.6 0.280 0.39 15.50 0.069
## [482,] 9.4 0.300 0.56 2.80 0.080
## [483,] 10.6 0.360 0.59 2.20 0.152
## [484,] 10.6 0.360 0.60 2.20 0.152
## [485,] 10.6 0.440 0.68 4.10 0.114
## [486,] 10.2 0.670 0.39 1.90 0.054
## [487,] 10.2 0.670 0.39 1.90 0.054
## [488,] 10.2 0.645 0.36 1.80 0.053
## [489,] 11.6 0.320 0.55 2.80 0.081
## [490,] 9.3 0.390 0.40 2.60 0.073
## [491,] 9.3 0.775 0.27 2.80 0.078
## [492,] 9.2 0.410 0.50 2.50 0.055
## [493,] 8.9 0.400 0.51 2.60 0.052
## [494,] 8.7 0.690 0.31 3.00 0.086
## [495,] 6.5 0.390 0.23 8.30 0.051
## [496,] 10.7 0.350 0.53 2.60 0.070
## [497,] 7.8 0.520 0.25 1.90 0.081
## [498,] 7.2 0.340 0.32 2.50 0.090
## [499,] 10.7 0.350 0.53 2.60 0.070
## [500,] 8.7 0.690 0.31 3.00 0.086
## [501,] 7.8 0.520 0.25 1.90 0.081
## [502,] 10.4 0.440 0.73 6.55 0.074
## [503,] 10.4 0.440 0.73 6.55 0.074
## [504,] 10.5 0.260 0.47 1.90 0.078
## [505,] 10.5 0.240 0.42 1.80 0.077
## [506,] 10.2 0.490 0.63 2.90 0.072
## [507,] 10.4 0.240 0.46 1.80 0.075
## [508,] 11.2 0.670 0.55 2.30 0.084
## [509,] 10.0 0.590 0.31 2.20 0.090
## [510,] 13.3 0.290 0.75 2.80 0.084
## [511,] 12.4 0.420 0.49 4.60 0.073
## [512,] 10.0 0.590 0.31 2.20 0.090
## [513,] 10.7 0.400 0.48 2.10 0.125
## [514,] 10.5 0.510 0.64 2.40 0.107
## [515,] 10.5 0.510 0.64 2.40 0.107
## [516,] 8.5 0.655 0.49 6.10 0.122
## [517,] 12.5 0.600 0.49 4.30 0.100
## [518,] 10.4 0.610 0.49 2.10 0.200
## [519,] 10.9 0.210 0.49 2.80 0.088
## [520,] 7.3 0.365 0.49 2.50 0.088
## [521,] 9.8 0.250 0.49 2.70 0.088
## [522,] 7.6 0.410 0.49 2.00 0.088
## [523,] 8.2 0.390 0.49 2.30 0.099
## [524,] 9.3 0.400 0.49 2.50 0.085
## [525,] 9.2 0.430 0.49 2.40 0.086
## [526,] 10.4 0.640 0.24 2.80 0.105
## [527,] 7.3 0.365 0.49 2.50 0.088
## [528,] 7.0 0.380 0.49 2.50 0.097
## [529,] 8.2 0.420 0.49 2.60 0.084
## [530,] 9.9 0.630 0.24 2.40 0.077
## [531,] 9.1 0.220 0.24 2.10 0.078
## [532,] 11.9 0.380 0.49 2.70 0.098
## [533,] 11.9 0.380 0.49 2.70 0.098
## [534,] 10.3 0.270 0.24 2.10 0.072
## [535,] 10.0 0.480 0.24 2.70 0.102
## [536,] 9.1 0.220 0.24 2.10 0.078
## [537,] 9.9 0.630 0.24 2.40 0.077
## [538,] 8.1 0.825 0.24 2.10 0.084
## [539,] 12.9 0.350 0.49 5.80 0.066
## [540,] 11.2 0.500 0.74 5.15 0.100
## [541,] 9.2 0.590 0.24 3.30 0.101
## [542,] 9.5 0.460 0.49 6.30 0.064
## [543,] 9.3 0.715 0.24 2.10 0.070
## [544,] 11.2 0.660 0.24 2.50 0.085
## [545,] 14.3 0.310 0.74 1.80 0.075
## [546,] 9.1 0.470 0.49 2.60 0.094
## [547,] 7.5 0.550 0.24 2.00 0.078
## [548,] 10.6 0.310 0.49 2.50 0.067
## [549,] 12.4 0.350 0.49 2.60 0.079
## [550,] 9.0 0.530 0.49 1.90 0.171
## [551,] 6.8 0.510 0.01 2.10 0.074
## [552,] 9.4 0.430 0.24 2.80 0.092
## [553,] 9.5 0.460 0.24 2.70 0.092
## [554,] 5.0 1.040 0.24 1.60 0.050
## [555,] 15.5 0.645 0.49 4.20 0.095
## [556,] 15.5 0.645 0.49 4.20 0.095
## [557,] 10.9 0.530 0.49 4.60 0.118
## [558,] 15.6 0.645 0.49 4.20 0.095
## [559,] 10.9 0.530 0.49 4.60 0.118
## [560,] 13.0 0.470 0.49 4.30 0.085
## [561,] 12.7 0.600 0.49 2.80 0.075
## [562,] 9.0 0.440 0.49 2.40 0.078
## [563,] 9.0 0.540 0.49 2.90 0.094
## [564,] 7.6 0.290 0.49 2.70 0.092
## [565,] 13.0 0.470 0.49 4.30 0.085
## [566,] 12.7 0.600 0.49 2.80 0.075
## [567,] 8.7 0.700 0.24 2.50 0.226
## [568,] 8.7 0.700 0.24 2.50 0.226
## [569,] 9.8 0.500 0.49 2.60 0.250
## [570,] 6.2 0.360 0.24 2.20 0.095
## [571,] 11.5 0.350 0.49 3.30 0.070
## [572,] 6.2 0.360 0.24 2.20 0.095
## [573,] 10.2 0.240 0.49 2.40 0.075
## [574,] 10.5 0.590 0.49 2.10 0.070
## [575,] 10.6 0.340 0.49 3.20 0.078
## [576,] 12.3 0.270 0.49 3.10 0.079
## [577,] 9.9 0.500 0.24 2.30 0.103
## [578,] 8.8 0.440 0.49 2.80 0.083
## [579,] 8.8 0.470 0.49 2.90 0.085
## [580,] 10.6 0.310 0.49 2.20 0.063
## [581,] 12.3 0.500 0.49 2.20 0.089
## [582,] 12.3 0.500 0.49 2.20 0.089
## [583,] 11.7 0.490 0.49 2.20 0.083
## [584,] 12.0 0.280 0.49 1.90 0.074
## [585,] 11.8 0.330 0.49 3.40 0.093
## [586,] 7.6 0.510 0.24 2.40 0.091
## [587,] 11.1 0.310 0.49 2.70 0.094
## [588,] 7.3 0.730 0.24 1.90 0.108
## [589,] 5.0 0.420 0.24 2.00 0.060
## [590,] 10.2 0.290 0.49 2.60 0.059
## [591,] 9.0 0.450 0.49 2.60 0.084
## [592,] 6.6 0.390 0.49 1.70 0.070
## [593,] 9.0 0.450 0.49 2.60 0.084
## [594,] 9.9 0.490 0.58 3.50 0.094
## [595,] 7.9 0.720 0.17 2.60 0.096
## [596,] 8.9 0.595 0.41 7.90 0.086
## [597,] 12.4 0.400 0.51 2.00 0.059
## [598,] 11.9 0.580 0.58 1.90 0.071
## [599,] 8.5 0.585 0.18 2.10 0.078
## [600,] 12.7 0.590 0.45 2.30 0.082
## [601,] 8.2 0.915 0.27 2.10 0.088
## [602,] 13.2 0.460 0.52 2.20 0.071
## [603,] 7.7 0.835 0.00 2.60 0.081
## [604,] 13.2 0.460 0.52 2.20 0.071
## [605,] 8.3 0.580 0.13 2.90 0.096
## [606,] 8.3 0.600 0.13 2.60 0.085
## [607,] 9.4 0.410 0.48 4.60 0.072
## [608,] 8.8 0.480 0.41 3.30 0.092
## [609,] 10.1 0.650 0.37 5.10 0.110
## [610,] 6.3 0.360 0.19 3.20 0.075
## [611,] 8.8 0.240 0.54 2.50 0.083
## [612,] 13.2 0.380 0.55 2.70 0.081
## [613,] 7.5 0.640 0.00 2.40 0.077
## [614,] 8.2 0.390 0.38 1.50 0.058
## [615,] 9.2 0.755 0.18 2.20 0.148
## [616,] 9.6 0.600 0.50 2.30 0.079
## [617,] 9.6 0.600 0.50 2.30 0.079
## [618,] 11.5 0.310 0.51 2.20 0.079
## [619,] 11.4 0.460 0.50 2.70 0.122
## [620,] 11.3 0.370 0.41 2.30 0.088
## [621,] 8.3 0.540 0.24 3.40 0.076
## [622,] 8.2 0.560 0.23 3.40 0.078
## [623,] 10.0 0.580 0.22 1.90 0.080
## [624,] 7.9 0.510 0.25 2.90 0.077
## [625,] 6.8 0.690 0.00 5.60 0.124
## [626,] 6.8 0.690 0.00 5.60 0.124
## [627,] 8.8 0.600 0.29 2.20 0.098
## [628,] 8.8 0.600 0.29 2.20 0.098
## [629,] 8.7 0.540 0.26 2.50 0.097
## [630,] 7.6 0.685 0.23 2.30 0.111
## [631,] 8.7 0.540 0.26 2.50 0.097
## [632,] 10.4 0.280 0.54 2.70 0.105
## [633,] 7.6 0.410 0.14 3.00 0.087
## [634,] 10.1 0.935 0.22 3.40 0.105
## [635,] 7.9 0.350 0.21 1.90 0.073
## [636,] 8.7 0.840 0.00 1.40 0.065
## [637,] 9.6 0.880 0.28 2.40 0.086
## [638,] 9.5 0.885 0.27 2.30 0.084
## [639,] 7.7 0.915 0.12 2.20 0.143
## [640,] 8.9 0.290 0.35 1.90 0.067
## [641,] 9.9 0.540 0.45 2.30 0.071
## [642,] 9.5 0.590 0.44 2.30 0.071
## [643,] 9.9 0.540 0.45 2.30 0.071
## [644,] 9.5 0.590 0.44 2.30 0.071
## [645,] 9.9 0.540 0.45 2.30 0.071
## [646,] 7.8 0.640 0.10 6.00 0.115
## [647,] 7.3 0.670 0.05 3.60 0.107
## [648,] 8.3 0.845 0.01 2.20 0.070
## [649,] 8.7 0.480 0.30 2.80 0.066
## [650,] 6.7 0.420 0.27 8.60 0.068
## [651,] 10.7 0.430 0.39 2.20 0.106
## [652,] 9.8 0.880 0.25 2.50 0.104
## [653,] 15.9 0.360 0.65 7.50 0.096
## [654,] 9.4 0.330 0.59 2.80 0.079
## [655,] 8.6 0.470 0.47 2.40 0.074
## [656,] 9.7 0.550 0.17 2.90 0.087
## [657,] 10.7 0.430 0.39 2.20 0.106
## [658,] 12.0 0.500 0.59 1.40 0.073
## [659,] 7.2 0.520 0.07 1.40 0.074
## [660,] 7.1 0.840 0.02 4.40 0.096
## [661,] 7.2 0.520 0.07 1.40 0.074
## [662,] 7.5 0.420 0.31 1.60 0.080
## [663,] 7.2 0.570 0.06 1.60 0.076
## [664,] 10.1 0.280 0.46 1.80 0.050
## [665,] 12.1 0.400 0.52 2.00 0.092
## [666,] 9.4 0.590 0.14 2.00 0.084
## [667,] 8.3 0.490 0.36 1.80 0.222
## [668,] 11.3 0.340 0.45 2.00 0.082
## [669,] 10.0 0.730 0.43 2.30 0.059
## [670,] 11.3 0.340 0.45 2.00 0.082
## [671,] 6.9 0.400 0.24 2.50 0.083
## [672,] 8.2 0.730 0.21 1.70 0.074
## [673,] 9.8 1.240 0.34 2.00 0.079
## [674,] 8.2 0.730 0.21 1.70 0.074
## [675,] 10.8 0.400 0.41 2.20 0.084
## [676,] 9.3 0.410 0.39 2.20 0.064
## [677,] 10.8 0.400 0.41 2.20 0.084
## [678,] 8.6 0.800 0.11 2.30 0.084
## [679,] 8.3 0.780 0.10 2.60 0.081
## [680,] 10.8 0.260 0.45 3.30 0.060
## [681,] 13.3 0.430 0.58 1.90 0.070
## [682,] 8.0 0.450 0.23 2.20 0.094
## [683,] 8.5 0.460 0.31 2.25 0.078
## [684,] 8.1 0.780 0.23 2.60 0.059
## [685,] 9.8 0.980 0.32 2.30 0.078
## [686,] 8.1 0.780 0.23 2.60 0.059
## [687,] 7.1 0.650 0.18 1.80 0.070
## [688,] 9.1 0.640 0.23 3.10 0.095
## [689,] 7.7 0.660 0.04 1.60 0.039
## [690,] 8.1 0.380 0.48 1.80 0.157
## [691,] 7.4 1.185 0.00 4.25 0.097
## [692,] 9.2 0.920 0.24 2.60 0.087
## [693,] 8.6 0.490 0.51 2.00 0.422
## [694,] 9.0 0.480 0.32 2.80 0.084
## [695,] 9.0 0.470 0.31 2.70 0.084
## [696,] 5.1 0.470 0.02 1.30 0.034
## [697,] 7.0 0.650 0.02 2.10 0.066
## [698,] 7.0 0.650 0.02 2.10 0.066
## [699,] 9.4 0.615 0.28 3.20 0.087
## [700,] 11.8 0.380 0.55 2.10 0.071
## [701,] 10.6 1.020 0.43 2.90 0.076
## [702,] 7.0 0.650 0.02 2.10 0.066
## [703,] 7.0 0.640 0.02 2.10 0.067
## [704,] 7.5 0.380 0.48 2.60 0.073
## [705,] 9.1 0.765 0.04 1.60 0.078
## [706,] 8.4 1.035 0.15 6.00 0.073
## [707,] 7.0 0.780 0.08 2.00 0.093
## [708,] 7.4 0.490 0.19 3.00 0.077
## [709,] 7.8 0.545 0.12 2.50 0.068
## [710,] 9.7 0.310 0.47 1.60 0.062
## [711,] 10.6 1.025 0.43 2.80 0.080
## [712,] 8.9 0.565 0.34 3.00 0.093
## [713,] 8.7 0.690 0.00 3.20 0.084
## [714,] 8.0 0.430 0.36 2.30 0.075
## [715,] 9.9 0.740 0.28 2.60 0.078
## [716,] 7.2 0.490 0.18 2.70 0.069
## [717,] 8.0 0.430 0.36 2.30 0.075
## [718,] 7.6 0.460 0.11 2.60 0.079
## [719,] 8.4 0.560 0.04 2.00 0.082
## [720,] 7.1 0.660 0.00 3.90 0.086
## [721,] 8.4 0.560 0.04 2.00 0.082
## [722,] 8.9 0.480 0.24 2.85 0.094
## [723,] 7.6 0.420 0.08 2.70 0.084
## [724,] 7.1 0.310 0.30 2.20 0.053
## [725,] 7.5 1.115 0.10 3.10 0.086
## [726,] 9.0 0.660 0.17 3.00 0.077
## [727,] 8.1 0.720 0.09 2.80 0.084
## [728,] 6.4 0.570 0.02 1.80 0.067
## [729,] 6.4 0.570 0.02 1.80 0.067
## [730,] 6.4 0.865 0.03 3.20 0.071
## [731,] 9.5 0.550 0.66 2.30 0.387
## [732,] 8.9 0.875 0.13 3.45 0.088
## [733,] 7.3 0.835 0.03 2.10 0.092
## [734,] 7.0 0.450 0.34 2.70 0.082
## [735,] 7.7 0.560 0.20 2.00 0.075
## [736,] 7.7 0.965 0.10 2.10 0.112
## [737,] 7.7 0.965 0.10 2.10 0.112
## [738,] 8.2 0.590 0.00 2.50 0.093
## [739,] 9.0 0.460 0.23 2.80 0.092
## [740,] 9.0 0.690 0.00 2.40 0.088
## [741,] 8.3 0.760 0.29 4.20 0.075
## [742,] 9.2 0.530 0.24 2.60 0.078
## [743,] 6.5 0.615 0.00 1.90 0.065
## [744,] 11.6 0.410 0.58 2.80 0.096
## [745,] 11.1 0.390 0.54 2.70 0.095
## [746,] 7.3 0.510 0.18 2.10 0.070
## [747,] 8.2 0.340 0.38 2.50 0.080
## [748,] 8.6 0.330 0.40 2.60 0.083
## [749,] 7.2 0.500 0.18 2.10 0.071
## [750,] 7.3 0.510 0.18 2.10 0.070
## [751,] 8.3 0.650 0.10 2.90 0.089
## [752,] 8.3 0.650 0.10 2.90 0.089
## [753,] 7.6 0.540 0.13 2.50 0.097
## [754,] 8.3 0.650 0.10 2.90 0.089
## [755,] 7.8 0.480 0.68 1.70 0.415
## [756,] 7.8 0.910 0.07 1.90 0.058
## [757,] 6.3 0.980 0.01 2.00 0.057
## [758,] 8.1 0.870 0.00 2.20 0.084
## [759,] 8.1 0.870 0.00 2.20 0.084
## [760,] 8.8 0.420 0.21 2.50 0.092
## [761,] 9.0 0.580 0.25 2.80 0.075
## [762,] 9.3 0.655 0.26 2.00 0.096
## [763,] 8.8 0.700 0.00 1.70 0.069
## [764,] 9.3 0.655 0.26 2.00 0.096
## [765,] 9.1 0.680 0.11 2.80 0.093
## [766,] 9.2 0.670 0.10 3.00 0.091
## [767,] 8.8 0.590 0.18 2.90 0.089
## [768,] 7.5 0.600 0.32 2.70 0.103
## [769,] 7.1 0.590 0.02 2.30 0.082
## [770,] 7.9 0.720 0.01 1.90 0.076
## [771,] 7.1 0.590 0.02 2.30 0.082
## [772,] 9.4 0.685 0.26 2.40 0.082
## [773,] 9.5 0.570 0.27 2.30 0.082
## [774,] 7.9 0.400 0.29 1.80 0.157
## [775,] 7.9 0.400 0.30 1.80 0.157
## [776,] 7.2 1.000 0.00 3.00 0.102
## [777,] 6.9 0.765 0.18 2.40 0.243
## [778,] 6.9 0.635 0.17 2.40 0.241
## [779,] 8.3 0.430 0.30 3.40 0.079
## [780,] 7.1 0.520 0.03 2.60 0.076
## [781,] 7.0 0.570 0.00 2.00 0.190
## [782,] 6.5 0.460 0.14 2.40 0.114
## [783,] 9.0 0.820 0.05 2.40 0.081
## [784,] 6.5 0.460 0.14 2.40 0.114
## [785,] 7.1 0.590 0.01 2.50 0.077
## [786,] 9.9 0.350 0.41 2.30 0.083
## [787,] 9.9 0.350 0.41 2.30 0.083
## [788,] 10.0 0.560 0.24 2.20 0.079
## [789,] 10.0 0.560 0.24 2.20 0.079
## [790,] 8.6 0.630 0.17 2.90 0.099
## [791,] 7.4 0.370 0.43 2.60 0.082
## [792,] 8.8 0.640 0.17 2.90 0.084
## [793,] 7.1 0.610 0.02 2.50 0.081
## [794,] 7.7 0.600 0.00 2.60 0.055
## [795,] 10.1 0.270 0.54 2.30 0.065
## [796,] 10.8 0.890 0.30 2.60 0.132
## [797,] 8.7 0.460 0.31 2.50 0.126
## [798,] 9.3 0.370 0.44 1.60 0.038
## [799,] 9.4 0.500 0.34 3.60 0.082
## [800,] 9.4 0.500 0.34 3.60 0.082
## [801,] 7.2 0.610 0.08 4.00 0.082
## [802,] 8.6 0.550 0.09 3.30 0.068
## [803,] 5.1 0.585 0.00 1.70 0.044
## [804,] 7.7 0.560 0.08 2.50 0.114
## [805,] 8.4 0.520 0.22 2.70 0.084
## [806,] 8.2 0.280 0.40 2.40 0.052
## [807,] 8.4 0.250 0.39 2.00 0.041
## [808,] 8.2 0.280 0.40 2.40 0.052
## [809,] 7.4 0.530 0.12 1.90 0.165
## [810,] 7.6 0.480 0.31 2.80 0.070
## [811,] 7.3 0.490 0.10 2.60 0.068
## [812,] 12.9 0.500 0.55 2.80 0.072
## [813,] 10.8 0.450 0.33 2.50 0.099
## [814,] 6.9 0.390 0.24 2.10 0.102
## [815,] 12.6 0.410 0.54 2.80 0.103
## [816,] 10.8 0.450 0.33 2.50 0.099
## [817,] 9.8 0.510 0.19 3.20 0.081
## [818,] 10.8 0.290 0.42 1.60 0.084
## [819,] 7.1 0.715 0.00 2.35 0.071
## [820,] 9.1 0.660 0.15 3.20 0.097
## [821,] 7.0 0.685 0.00 1.90 0.099
## [822,] 4.9 0.420 0.00 2.10 0.048
## [823,] 6.7 0.540 0.13 2.00 0.076
## [824,] 6.7 0.540 0.13 2.00 0.076
## [825,] 7.1 0.480 0.28 2.80 0.068
## [826,] 7.1 0.460 0.14 2.80 0.076
## [827,] 7.5 0.270 0.34 2.30 0.050
## [828,] 7.1 0.460 0.14 2.80 0.076
## [829,] 7.8 0.570 0.09 2.30 0.065
## [830,] 5.9 0.610 0.08 2.10 0.071
## [831,] 7.5 0.685 0.07 2.50 0.058
## [832,] 5.9 0.610 0.08 2.10 0.071
## [833,] 10.4 0.440 0.42 1.50 0.145
## [834,] 11.6 0.470 0.44 1.60 0.147
## [835,] 8.8 0.685 0.26 1.60 0.088
## [836,] 7.6 0.665 0.10 1.50 0.066
## [837,] 6.7 0.280 0.28 2.40 0.012
## [838,] 6.7 0.280 0.28 2.40 0.012
## [839,] 10.1 0.310 0.35 1.60 0.075
## [840,] 6.0 0.500 0.04 2.20 0.092
## [841,] 11.1 0.420 0.47 2.65 0.085
## [842,] 6.6 0.660 0.00 3.00 0.115
## [843,] 10.6 0.500 0.45 2.60 0.119
## [844,] 7.1 0.685 0.35 2.00 0.088
## [845,] 9.9 0.250 0.46 1.70 0.062
## [846,] 6.4 0.640 0.21 1.80 0.081
## [847,] 6.4 0.640 0.21 1.80 0.081
## [848,] 7.4 0.680 0.16 1.80 0.078
## [849,] 6.4 0.640 0.21 1.80 0.081
## [850,] 6.4 0.630 0.21 1.60 0.080
## [851,] 9.3 0.430 0.44 1.90 0.085
## [852,] 9.3 0.430 0.44 1.90 0.085
## [853,] 8.0 0.420 0.32 2.50 0.080
## [854,] 9.3 0.360 0.39 1.50 0.080
## [855,] 9.3 0.360 0.39 1.50 0.080
## [856,] 7.6 0.735 0.02 2.50 0.071
## [857,] 9.3 0.360 0.39 1.50 0.080
## [858,] 8.2 0.260 0.34 2.50 0.073
## [859,] 11.7 0.280 0.47 1.70 0.054
## [860,] 6.8 0.560 0.22 1.80 0.074
## [861,] 7.2 0.620 0.06 2.70 0.077
## [862,] 5.8 1.010 0.66 2.00 0.039
## [863,] 7.5 0.420 0.32 2.70 0.067
## [864,] 7.2 0.620 0.06 2.50 0.078
## [865,] 7.2 0.620 0.06 2.70 0.077
## [866,] 7.2 0.635 0.07 2.60 0.077
## [867,] 6.8 0.490 0.22 2.30 0.071
## [868,] 6.9 0.510 0.23 2.00 0.072
## [869,] 6.8 0.560 0.22 1.80 0.074
## [870,] 7.6 0.630 0.03 2.00 0.080
## [871,] 7.7 0.715 0.01 2.10 0.064
## [872,] 6.9 0.560 0.03 1.50 0.086
## [873,] 7.3 0.350 0.24 2.00 0.067
## [874,] 9.1 0.210 0.37 1.60 0.067
## [875,] 10.4 0.380 0.46 2.10 0.104
## [876,] 8.8 0.310 0.40 2.80 0.109
## [877,] 7.1 0.470 0.00 2.20 0.067
## [878,] 7.7 0.715 0.01 2.10 0.064
## [879,] 8.8 0.610 0.19 4.00 0.094
## [880,] 7.2 0.600 0.04 2.50 0.076
## [881,] 9.2 0.560 0.18 1.60 0.078
## [882,] 7.6 0.715 0.00 2.10 0.068
## [883,] 8.4 0.310 0.29 3.10 0.194
## [884,] 7.2 0.600 0.04 2.50 0.076
## [885,] 8.8 0.610 0.19 4.00 0.094
## [886,] 8.9 0.750 0.14 2.50 0.086
## [887,] 9.0 0.800 0.12 2.40 0.083
## [888,] 10.7 0.520 0.38 2.60 0.066
## [889,] 6.8 0.570 0.00 2.50 0.072
## [890,] 10.7 0.900 0.34 6.60 0.112
## [891,] 7.2 0.340 0.24 2.00 0.071
## [892,] 7.2 0.660 0.03 2.30 0.078
## [893,] 10.1 0.450 0.23 1.90 0.082
## [894,] 7.2 0.660 0.03 2.30 0.078
## [895,] 7.2 0.630 0.03 2.20 0.080
## [896,] 7.1 0.590 0.01 2.30 0.080
## [897,] 8.3 0.310 0.39 2.40 0.078
## [898,] 7.1 0.590 0.01 2.30 0.080
## [899,] 8.3 0.310 0.39 2.40 0.078
## [900,] 8.3 1.020 0.02 3.40 0.084
## [901,] 8.9 0.310 0.36 2.60 0.056
## [902,] 7.4 0.635 0.10 2.40 0.080
## [903,] 7.4 0.635 0.10 2.40 0.080
## [904,] 6.8 0.590 0.06 6.00 0.060
## [905,] 6.8 0.590 0.06 6.00 0.060
## [906,] 9.2 0.580 0.20 3.00 0.081
## [907,] 7.2 0.540 0.27 2.60 0.084
## [908,] 6.1 0.560 0.00 2.20 0.079
## [909,] 7.4 0.520 0.13 2.40 0.078
## [910,] 7.3 0.305 0.39 1.20 0.059
## [911,] 9.3 0.380 0.48 3.80 0.132
## [912,] 9.1 0.280 0.46 9.00 0.114
## [913,] 10.0 0.460 0.44 2.90 0.065
## [914,] 9.4 0.395 0.46 4.60 0.094
## [915,] 7.3 0.305 0.39 1.20 0.059
## [916,] 8.6 0.315 0.40 2.20 0.079
## [917,] 5.3 0.715 0.19 1.50 0.161
## [918,] 6.8 0.410 0.31 8.80 0.084
## [919,] 8.4 0.360 0.32 2.20 0.081
## [920,] 8.4 0.620 0.12 1.80 0.072
## [921,] 9.6 0.410 0.37 2.30 0.091
## [922,] 8.4 0.360 0.32 2.20 0.081
## [923,] 8.4 0.620 0.12 1.80 0.072
## [924,] 6.8 0.410 0.31 8.80 0.084
## [925,] 8.6 0.470 0.27 2.30 0.055
## [926,] 8.6 0.220 0.36 1.90 0.064
## [927,] 9.4 0.240 0.33 2.30 0.061
## [928,] 8.4 0.670 0.19 2.20 0.093
## [929,] 8.6 0.470 0.27 2.30 0.055
## [930,] 8.7 0.330 0.38 3.30 0.063
## [931,] 6.6 0.610 0.01 1.90 0.080
## [932,] 7.4 0.610 0.01 2.00 0.074
## [933,] 7.6 0.400 0.29 1.90 0.078
## [934,] 7.4 0.610 0.01 2.00 0.074
## [935,] 6.6 0.610 0.01 1.90 0.080
## [936,] 8.8 0.300 0.38 2.30 0.060
## [937,] 8.8 0.300 0.38 2.30 0.060
## [938,] 12.0 0.630 0.50 1.40 0.071
## [939,] 7.2 0.380 0.38 2.80 0.068
## [940,] 6.2 0.460 0.17 1.60 0.073
## [941,] 9.6 0.330 0.52 2.20 0.074
## [942,] 9.9 0.270 0.49 5.00 0.082
## [943,] 10.1 0.430 0.40 2.60 0.092
## [944,] 9.8 0.500 0.34 2.30 0.094
## [945,] 8.3 0.300 0.49 3.80 0.090
## [946,] 10.2 0.440 0.42 2.00 0.071
## [947,] 10.2 0.440 0.58 4.10 0.092
## [948,] 8.3 0.280 0.48 2.10 0.093
## [949,] 8.9 0.120 0.45 1.80 0.075
## [950,] 8.9 0.120 0.45 1.80 0.075
## [951,] 8.9 0.120 0.45 1.80 0.075
## [952,] 8.3 0.280 0.48 2.10 0.093
## [953,] 8.2 0.310 0.40 2.20 0.058
## [954,] 10.2 0.340 0.48 2.10 0.052
## [955,] 7.6 0.430 0.40 2.70 0.082
## [956,] 8.5 0.210 0.52 1.90 0.090
## [957,] 9.0 0.360 0.52 2.10 0.111
## [958,] 9.5 0.370 0.52 2.00 0.088
## [959,] 6.4 0.570 0.12 2.30 0.120
## [960,] 8.0 0.590 0.05 2.00 0.089
## [961,] 8.5 0.470 0.27 1.90 0.058
## [962,] 7.1 0.560 0.14 1.60 0.078
## [963,] 6.6 0.570 0.02 2.10 0.115
## [964,] 8.8 0.270 0.39 2.00 0.100
## [965,] 8.5 0.470 0.27 1.90 0.058
## [966,] 8.3 0.340 0.40 2.40 0.065
## [967,] 9.0 0.380 0.41 2.40 0.103
## [968,] 8.5 0.660 0.20 2.10 0.097
## [969,] 9.0 0.400 0.43 2.40 0.068
## [970,] 6.7 0.560 0.09 2.90 0.079
## [971,] 10.4 0.260 0.48 1.90 0.066
## [972,] 10.4 0.260 0.48 1.90 0.066
## [973,] 10.1 0.380 0.50 2.40 0.104
## [974,] 8.5 0.340 0.44 1.70 0.079
## [975,] 8.8 0.330 0.41 5.90 0.073
## [976,] 7.2 0.410 0.30 2.10 0.083
## [977,] 7.2 0.410 0.30 2.10 0.083
## [978,] 8.4 0.590 0.29 2.60 0.109
## [979,] 7.0 0.400 0.32 3.60 0.061
## [980,] 12.2 0.450 0.49 1.40 0.075
## [981,] 9.1 0.500 0.30 1.90 0.065
## [982,] 9.5 0.860 0.26 1.90 0.079
## [983,] 7.3 0.520 0.32 2.10 0.070
## [984,] 9.1 0.500 0.30 1.90 0.065
## [985,] 12.2 0.450 0.49 1.40 0.075
## [986,] 7.4 0.580 0.00 2.00 0.064
## [987,] 9.8 0.340 0.39 1.40 0.066
## [988,] 7.1 0.360 0.30 1.60 0.080
## [989,] 7.7 0.390 0.12 1.70 0.097
## [990,] 9.7 0.295 0.40 1.50 0.073
## [991,] 7.7 0.390 0.12 1.70 0.097
## [992,] 7.1 0.340 0.28 2.00 0.082
## [993,] 6.5 0.400 0.10 2.00 0.076
## [994,] 7.1 0.340 0.28 2.00 0.082
## [995,] 10.0 0.350 0.45 2.50 0.092
## [996,] 7.7 0.600 0.06 2.00 0.079
## [997,] 5.6 0.660 0.00 2.20 0.087
## [998,] 5.6 0.660 0.00 2.20 0.087
## [999,] 8.9 0.840 0.34 1.40 0.050
## [1000,] 6.4 0.690 0.00 1.65 0.055
## [1001,] 7.5 0.430 0.30 2.20 0.062
## [1002,] 9.9 0.350 0.38 1.50 0.058
## [1003,] 9.1 0.290 0.33 2.05 0.063
## [1004,] 6.8 0.360 0.32 1.80 0.067
## [1005,] 8.2 0.430 0.29 1.60 0.081
## [1006,] 6.8 0.360 0.32 1.80 0.067
## [1007,] 9.1 0.290 0.33 2.05 0.063
## [1008,] 9.1 0.300 0.34 2.00 0.064
## [1009,] 8.9 0.350 0.40 3.60 0.110
## [1010,] 9.6 0.500 0.36 2.80 0.116
## [1011,] 8.9 0.280 0.45 1.70 0.067
## [1012,] 8.9 0.320 0.31 2.00 0.088
## [1013,] 7.7 1.005 0.15 2.10 0.102
## [1014,] 7.5 0.710 0.00 1.60 0.092
## [1015,] 8.0 0.580 0.16 2.00 0.120
## [1016,] 10.5 0.390 0.46 2.20 0.075
## [1017,] 8.9 0.380 0.40 2.20 0.068
## [1018,] 8.0 0.180 0.37 0.90 0.049
## [1019,] 8.0 0.180 0.37 0.90 0.049
## [1020,] 7.0 0.500 0.14 1.80 0.078
## [1021,] 11.3 0.360 0.66 2.40 0.123
## [1022,] 11.3 0.360 0.66 2.40 0.123
## [1023,] 7.0 0.510 0.09 2.10 0.062
## [1024,] 8.2 0.320 0.42 2.30 0.098
## [1025,] 7.7 0.580 0.01 1.80 0.088
## [1026,] 8.6 0.830 0.00 2.80 0.095
## [1027,] 7.9 0.310 0.32 1.90 0.066
## [1028,] 6.4 0.795 0.00 2.20 0.065
## [1029,] 7.2 0.340 0.21 2.50 0.075
## [1030,] 7.7 0.580 0.01 1.80 0.088
## [1031,] 7.1 0.590 0.00 2.10 0.091
## [1032,] 7.3 0.550 0.01 1.80 0.093
## [1033,] 8.1 0.820 0.00 4.10 0.095
## [1034,] 7.5 0.570 0.08 2.60 0.089
## [1035,] 8.9 0.745 0.18 2.50 0.077
## [1036,] 10.1 0.370 0.34 2.40 0.085
## [1037,] 7.6 0.310 0.34 2.50 0.082
## [1038,] 7.3 0.910 0.10 1.80 0.074
## [1039,] 8.7 0.410 0.41 6.20 0.078
## [1040,] 8.9 0.500 0.21 2.20 0.088
## [1041,] 7.4 0.965 0.00 2.20 0.088
## [1042,] 6.9 0.490 0.19 1.70 0.079
## [1043,] 8.9 0.500 0.21 2.20 0.088
## [1044,] 9.5 0.390 0.41 8.90 0.069
## [1045,] 6.4 0.390 0.33 3.30 0.046
## [1046,] 6.9 0.440 0.00 1.40 0.070
## [1047,] 7.6 0.780 0.00 1.70 0.076
## [1048,] 7.1 0.430 0.17 1.80 0.082
## [1049,] 9.3 0.490 0.36 1.70 0.081
## [1050,] 9.3 0.500 0.36 1.80 0.084
## [1051,] 7.1 0.430 0.17 1.80 0.082
## [1052,] 8.5 0.460 0.59 1.40 0.414
## [1053,] 5.6 0.605 0.05 2.40 0.073
## [1054,] 8.3 0.330 0.42 2.30 0.070
## [1055,] 8.2 0.640 0.27 2.00 0.095
## [1056,] 8.2 0.640 0.27 2.00 0.095
## [1057,] 8.9 0.480 0.53 4.00 0.101
## [1058,] 7.6 0.420 0.25 3.90 0.104
## [1059,] 9.9 0.530 0.57 2.40 0.093
## [1060,] 8.9 0.480 0.53 4.00 0.101
## [1061,] 11.6 0.230 0.57 1.80 0.074
## [1062,] 9.1 0.400 0.50 1.80 0.071
## [1063,] 8.0 0.380 0.44 1.90 0.098
## [1064,] 10.2 0.290 0.65 2.40 0.075
## [1065,] 8.2 0.740 0.09 2.00 0.067
## [1066,] 7.7 0.610 0.18 2.40 0.083
## [1067,] 6.6 0.520 0.08 2.40 0.070
## [1068,] 11.1 0.310 0.53 2.20 0.060
## [1069,] 11.1 0.310 0.53 2.20 0.060
## [1070,] 8.0 0.620 0.35 2.80 0.086
## [1071,] 9.3 0.330 0.45 1.50 0.057
## [1072,] 7.5 0.770 0.20 8.10 0.098
## [1073,] 7.2 0.350 0.26 1.80 0.083
## [1074,] 8.0 0.620 0.33 2.70 0.088
## [1075,] 7.5 0.770 0.20 8.10 0.098
## [1076,] 9.1 0.250 0.34 2.00 0.071
## [1077,] 9.9 0.320 0.56 2.00 0.073
## [1078,] 8.6 0.370 0.65 6.40 0.080
## [1079,] 8.6 0.370 0.65 6.40 0.080
## [1080,] 7.9 0.300 0.68 8.30 0.050
## [1081,] 10.3 0.270 0.56 1.40 0.047
## [1082,] 7.9 0.300 0.68 8.30 0.050
## [1083,] 7.2 0.380 0.30 1.80 0.073
## [1084,] 8.7 0.420 0.45 2.40 0.072
## [1085,] 7.2 0.380 0.30 1.80 0.073
## [1086,] 6.8 0.480 0.08 1.80 0.074
## [1087,] 8.5 0.340 0.40 4.70 0.055
## [1088,] 7.9 0.190 0.42 1.60 0.057
## [1089,] 11.6 0.410 0.54 1.50 0.095
## [1090,] 11.6 0.410 0.54 1.50 0.095
## [1091,] 10.0 0.260 0.54 1.90 0.083
## [1092,] 7.9 0.340 0.42 2.00 0.086
## [1093,] 7.0 0.540 0.09 2.00 0.081
## [1094,] 9.2 0.310 0.36 2.20 0.079
## [1095,] 6.6 0.725 0.09 5.50 0.117
## [1096,] 9.4 0.400 0.47 2.50 0.087
## [1097,] 6.6 0.725 0.09 5.50 0.117
## [1098,] 8.6 0.520 0.38 1.50 0.096
## [1099,] 8.0 0.310 0.45 2.10 0.216
## [1100,] 8.6 0.520 0.38 1.50 0.096
## [1101,] 8.4 0.340 0.42 2.10 0.072
## [1102,] 7.4 0.490 0.27 2.10 0.071
## [1103,] 6.1 0.480 0.09 1.70 0.078
## [1104,] 7.4 0.490 0.27 2.10 0.071
## [1105,] 8.0 0.480 0.34 2.20 0.073
## [1106,] 6.3 0.570 0.28 2.10 0.048
## [1107,] 8.2 0.230 0.42 1.90 0.069
## [1108,] 9.1 0.300 0.41 2.00 0.068
## [1109,] 8.1 0.780 0.10 3.30 0.090
## [1110,] 10.8 0.470 0.43 2.10 0.171
## [1111,] 8.3 0.530 0.00 1.40 0.070
## [1112,] 5.4 0.420 0.27 2.00 0.092
## [1113,] 7.9 0.330 0.41 1.50 0.056
## [1114,] 8.9 0.240 0.39 1.60 0.074
## [1115,] 5.0 0.400 0.50 4.30 0.046
## [1116,] 7.0 0.690 0.07 2.50 0.091
## [1117,] 7.0 0.690 0.07 2.50 0.091
## [1118,] 7.0 0.690 0.07 2.50 0.091
## [1119,] 7.1 0.390 0.12 2.10 0.065
## [1120,] 5.6 0.660 0.00 2.50 0.066
## [1121,] 7.9 0.540 0.34 2.50 0.076
## [1122,] 6.6 0.500 0.00 1.80 0.062
## [1123,] 6.3 0.470 0.00 1.40 0.055
## [1124,] 10.7 0.400 0.37 1.90 0.081
## [1125,] 6.5 0.580 0.00 2.20 0.096
## [1126,] 8.8 0.240 0.35 1.70 0.055
## [1127,] 5.8 0.290 0.26 1.70 0.063
## [1128,] 6.3 0.760 0.00 2.90 0.072
## [1129,] 10.0 0.430 0.33 2.70 0.095
## [1130,] 10.5 0.430 0.35 3.30 0.092
## [1131,] 9.1 0.600 0.00 1.90 0.058
## [1132,] 5.9 0.190 0.21 1.70 0.045
## [1133,] 7.4 0.360 0.34 1.80 0.075
## [1134,] 7.2 0.480 0.07 5.50 0.089
## [1135,] 8.5 0.280 0.35 1.70 0.061
## [1136,] 8.0 0.250 0.43 1.70 0.067
## [1137,] 10.4 0.520 0.45 2.00 0.080
## [1138,] 10.4 0.520 0.45 2.00 0.080
## [1139,] 7.5 0.410 0.15 3.70 0.104
## [1140,] 8.2 0.510 0.24 2.00 0.079
## [1141,] 7.3 0.400 0.30 1.70 0.080
## [1142,] 8.2 0.380 0.32 2.50 0.080
## [1143,] 6.9 0.450 0.11 2.40 0.043
## [1144,] 7.0 0.220 0.30 1.80 0.065
## [1145,] 7.3 0.320 0.23 2.30 0.066
## [1146,] 8.2 0.200 0.43 2.50 0.076
## [1147,] 7.8 0.500 0.12 1.80 0.178
## [1148,] 10.0 0.410 0.45 6.20 0.071
## [1149,] 7.8 0.390 0.42 2.00 0.086
## [1150,] 10.0 0.350 0.47 2.00 0.061
## [1151,] 8.2 0.330 0.32 2.80 0.067
## [1152,] 6.1 0.580 0.23 2.50 0.044
## [1153,] 8.3 0.600 0.25 2.20 0.118
## [1154,] 9.6 0.420 0.35 2.10 0.083
## [1155,] 6.6 0.580 0.00 2.20 0.100
## [1156,] 8.3 0.600 0.25 2.20 0.118
## [1157,] 8.5 0.180 0.51 1.75 0.071
## [1158,] 5.1 0.510 0.18 2.10 0.042
## [1159,] 6.7 0.410 0.43 2.80 0.076
## [1160,] 10.2 0.410 0.43 2.20 0.110
## [1161,] 10.6 0.360 0.57 2.30 0.087
## [1162,] 8.8 0.450 0.43 1.40 0.076
## [1163,] 8.5 0.320 0.42 2.30 0.075
## [1164,] 9.0 0.785 0.24 1.70 0.078
## [1165,] 9.0 0.785 0.24 1.70 0.078
## [1166,] 8.5 0.440 0.50 1.90 0.369
## [1167,] 9.9 0.540 0.26 2.00 0.111
## [1168,] 8.2 0.330 0.39 2.50 0.074
## [1169,] 6.5 0.340 0.27 2.80 0.067
## [1170,] 7.6 0.500 0.29 2.30 0.086
## [1171,] 9.2 0.360 0.34 1.60 0.062
## [1172,] 7.1 0.590 0.00 2.20 0.078
## [1173,] 9.7 0.420 0.46 2.10 0.074
## [1174,] 7.6 0.360 0.31 1.70 0.079
## [1175,] 7.6 0.360 0.31 1.70 0.079
## [1176,] 6.5 0.610 0.00 2.20 0.095
## [1177,] 6.5 0.880 0.03 5.60 0.079
## [1178,] 7.1 0.660 0.00 2.40 0.052
## [1179,] 5.6 0.915 0.00 2.10 0.041
## [1180,] 8.2 0.350 0.33 2.40 0.076
## [1181,] 8.2 0.350 0.33 2.40 0.076
## [1182,] 9.8 0.390 0.43 1.65 0.068
## [1183,] 10.2 0.400 0.40 2.50 0.068
## [1184,] 6.8 0.660 0.07 1.60 0.070
## [1185,] 6.7 0.640 0.23 2.10 0.080
## [1186,] 7.0 0.430 0.30 2.00 0.085
## [1187,] 6.6 0.800 0.03 7.80 0.079
## [1188,] 7.0 0.430 0.30 2.00 0.085
## [1189,] 6.7 0.640 0.23 2.10 0.080
## [1190,] 8.8 0.955 0.05 1.80 0.075
## [1191,] 9.1 0.400 0.57 4.60 0.080
## [1192,] 6.5 0.885 0.00 2.30 0.166
## [1193,] 7.2 0.250 0.37 2.50 0.063
## [1194,] 6.4 0.885 0.00 2.30 0.166
## [1195,] 7.0 0.745 0.12 1.80 0.114
## [1196,] 6.2 0.430 0.22 1.80 0.078
## [1197,] 7.9 0.580 0.23 2.30 0.076
## [1198,] 7.7 0.570 0.21 1.50 0.069
## [1199,] 7.7 0.260 0.26 2.00 0.052
## [1200,] 7.9 0.580 0.23 2.30 0.076
## [1201,] 7.7 0.570 0.21 1.50 0.069
## [1202,] 7.9 0.340 0.36 1.90 0.065
## [1203,] 8.6 0.420 0.39 1.80 0.068
## [1204,] 9.9 0.740 0.19 5.80 0.111
## [1205,] 7.2 0.360 0.46 2.10 0.074
## [1206,] 7.2 0.360 0.46 2.10 0.074
## [1207,] 7.2 0.360 0.46 2.10 0.074
## [1208,] 9.9 0.720 0.55 1.70 0.136
## [1209,] 7.2 0.360 0.46 2.10 0.074
## [1210,] 6.2 0.390 0.43 2.00 0.071
## [1211,] 6.8 0.650 0.02 2.10 0.078
## [1212,] 6.6 0.440 0.15 2.10 0.076
## [1213,] 6.8 0.650 0.02 2.10 0.078
## [1214,] 9.6 0.380 0.42 1.90 0.071
## [1215,] 10.2 0.330 0.46 1.90 0.081
## [1216,] 8.8 0.270 0.46 2.10 0.095
## [1217,] 7.9 0.570 0.31 2.00 0.079
## [1218,] 8.2 0.340 0.37 1.90 0.057
## [1219,] 8.2 0.400 0.31 1.90 0.082
## [1220,] 9.0 0.390 0.40 1.30 0.044
## [1221,] 10.9 0.320 0.52 1.80 0.132
## [1222,] 10.9 0.320 0.52 1.80 0.132
## [1223,] 8.1 0.530 0.22 2.20 0.078
## [1224,] 10.5 0.360 0.47 2.20 0.074
## [1225,] 12.6 0.390 0.49 2.50 0.080
## [1226,] 9.2 0.460 0.23 2.60 0.091
## [1227,] 7.5 0.580 0.03 4.10 0.080
## [1228,] 9.0 0.580 0.25 2.00 0.104
## [1229,] 5.1 0.420 0.00 1.80 0.044
## [1230,] 7.6 0.430 0.29 2.10 0.075
## [1231,] 7.7 0.180 0.34 2.70 0.066
## [1232,] 7.8 0.815 0.01 2.60 0.074
## [1233,] 7.6 0.430 0.29 2.10 0.075
## [1234,] 10.2 0.230 0.37 2.20 0.057
## [1235,] 7.1 0.750 0.01 2.20 0.059
## [1236,] 6.0 0.330 0.32 12.90 0.054
## [1237,] 7.8 0.550 0.00 1.70 0.070
## [1238,] 7.1 0.750 0.01 2.20 0.059
## [1239,] 8.1 0.730 0.00 2.50 0.081
## [1240,] 6.5 0.670 0.00 4.30 0.057
## [1241,] 7.5 0.610 0.20 1.70 0.076
## [1242,] 9.8 0.370 0.39 2.50 0.079
## [1243,] 9.0 0.400 0.41 2.00 0.058
## [1244,] 8.3 0.560 0.22 2.40 0.082
## [1245,] 5.9 0.290 0.25 13.40 0.067
## [1246,] 7.4 0.550 0.19 1.80 0.082
## [1247,] 7.4 0.740 0.07 1.70 0.086
## [1248,] 7.4 0.550 0.19 1.80 0.082
## [1249,] 6.9 0.410 0.33 2.20 0.081
## [1250,] 7.1 0.600 0.01 2.30 0.079
## [1251,] 7.1 0.600 0.01 2.30 0.079
## [1252,] 7.5 0.580 0.14 2.20 0.077
## [1253,] 7.1 0.720 0.00 1.80 0.123
## [1254,] 7.9 0.660 0.00 1.40 0.096
## [1255,] 7.8 0.700 0.06 1.90 0.079
## [1256,] 6.1 0.640 0.02 2.40 0.069
## [1257,] 7.5 0.590 0.22 1.80 0.082
## [1258,] 7.0 0.580 0.28 4.80 0.085
## [1259,] 6.8 0.640 0.00 2.70 0.123
## [1260,] 6.8 0.640 0.00 2.70 0.123
## [1261,] 8.6 0.635 0.68 1.80 0.403
## [1262,] 6.3 1.020 0.00 2.00 0.083
## [1263,] 9.8 0.450 0.38 2.50 0.081
## [1264,] 8.2 0.780 0.00 2.20 0.089
## [1265,] 8.5 0.370 0.32 1.80 0.066
## [1266,] 7.2 0.570 0.05 2.30 0.081
## [1267,] 7.2 0.570 0.05 2.30 0.081
## [1268,] 10.4 0.430 0.50 2.30 0.068
## [1269,] 6.9 0.410 0.31 2.00 0.079
## [1270,] 5.5 0.490 0.03 1.80 0.044
## [1271,] 5.0 0.380 0.01 1.60 0.048
## [1272,] 7.3 0.440 0.20 1.60 0.049
## [1273,] 5.9 0.460 0.00 1.90 0.077
## [1274,] 7.5 0.580 0.20 2.00 0.073
## [1275,] 7.8 0.580 0.13 2.10 0.102
## [1276,] 8.0 0.715 0.22 2.30 0.075
## [1277,] 8.5 0.400 0.40 6.30 0.050
## [1278,] 7.0 0.690 0.00 1.90 0.114
## [1279,] 8.0 0.715 0.22 2.30 0.075
## [1280,] 9.8 0.300 0.39 1.70 0.062
## [1281,] 7.1 0.460 0.20 1.90 0.077
## [1282,] 7.1 0.460 0.20 1.90 0.077
## [1283,] 7.9 0.765 0.00 2.00 0.084
## [1284,] 8.7 0.630 0.28 2.70 0.096
## [1285,] 7.0 0.420 0.19 2.30 0.071
## [1286,] 11.3 0.370 0.50 1.80 0.090
## [1287,] 7.1 0.160 0.44 2.50 0.068
## [1288,] 8.0 0.600 0.08 2.60 0.056
## [1289,] 7.0 0.600 0.30 4.50 0.068
## [1290,] 7.0 0.600 0.30 4.50 0.068
## [1291,] 7.6 0.740 0.00 1.90 0.100
## [1292,] 8.2 0.635 0.10 2.10 0.073
## [1293,] 5.9 0.395 0.13 2.40 0.056
## [1294,] 7.5 0.755 0.00 1.90 0.084
## [1295,] 8.2 0.635 0.10 2.10 0.073
## [1296,] 6.6 0.630 0.00 4.30 0.093
## [1297,] 6.6 0.630 0.00 4.30 0.093
## [1298,] 7.2 0.530 0.14 2.10 0.064
## [1299,] 5.7 0.600 0.00 1.40 0.063
## [1300,] 7.6 1.580 0.00 2.10 0.137
## [1301,] 5.2 0.645 0.00 2.15 0.080
## [1302,] 6.7 0.860 0.07 2.00 0.100
## [1303,] 9.1 0.370 0.32 2.10 0.064
## [1304,] 8.0 0.280 0.44 1.80 0.081
## [1305,] 7.6 0.790 0.21 2.30 0.087
## [1306,] 7.5 0.610 0.26 1.90 0.073
## [1307,] 9.7 0.690 0.32 2.50 0.088
## [1308,] 6.8 0.680 0.09 3.90 0.068
## [1309,] 9.7 0.690 0.32 2.50 0.088
## [1310,] 7.0 0.620 0.10 1.40 0.071
## [1311,] 7.5 0.610 0.26 1.90 0.073
## [1312,] 6.5 0.510 0.15 3.00 0.064
## [1313,] 8.0 1.180 0.21 1.90 0.083
## [1314,] 7.0 0.360 0.21 2.30 0.086
## [1315,] 7.0 0.360 0.21 2.40 0.086
## [1316,] 7.5 0.630 0.27 2.00 0.083
## [1317,] 5.4 0.740 0.00 1.20 0.041
## [1318,] 9.9 0.440 0.46 2.20 0.091
## [1319,] 7.5 0.630 0.27 2.00 0.083
## [1320,] 9.1 0.760 0.68 1.70 0.414
## [1321,] 9.7 0.660 0.34 2.60 0.094
## [1322,] 5.0 0.740 0.00 1.20 0.041
## [1323,] 9.1 0.340 0.42 1.80 0.058
## [1324,] 9.1 0.360 0.39 1.80 0.060
## [1325,] 6.7 0.460 0.24 1.70 0.077
## [1326,] 6.7 0.460 0.24 1.70 0.077
## [1327,] 6.7 0.460 0.24 1.70 0.077
## [1328,] 6.7 0.460 0.24 1.70 0.077
## [1329,] 6.5 0.520 0.11 1.80 0.073
## [1330,] 7.4 0.600 0.26 2.10 0.083
## [1331,] 7.4 0.600 0.26 2.10 0.083
## [1332,] 7.8 0.870 0.26 3.80 0.107
## [1333,] 8.4 0.390 0.10 1.70 0.075
## [1334,] 9.1 0.775 0.22 2.20 0.079
## [1335,] 7.2 0.835 0.00 2.00 0.166
## [1336,] 6.6 0.580 0.02 2.40 0.069
## [1337,] 6.0 0.500 0.00 1.40 0.057
## [1338,] 6.0 0.500 0.00 1.40 0.057
## [1339,] 6.0 0.500 0.00 1.40 0.057
## [1340,] 7.5 0.510 0.02 1.70 0.084
## [1341,] 7.5 0.510 0.02 1.70 0.084
## [1342,] 7.5 0.510 0.02 1.70 0.084
## [1343,] 7.6 0.540 0.02 1.70 0.085
## [1344,] 7.5 0.510 0.02 1.70 0.084
## [1345,] 11.5 0.420 0.48 2.60 0.077
## [1346,] 8.2 0.440 0.24 2.30 0.063
## [1347,] 6.1 0.590 0.01 2.10 0.056
## [1348,] 7.2 0.655 0.03 1.80 0.078
## [1349,] 7.2 0.655 0.03 1.80 0.078
## [1350,] 6.9 0.570 0.00 2.80 0.081
## [1351,] 9.0 0.600 0.29 2.00 0.069
## [1352,] 7.2 0.620 0.01 2.30 0.065
## [1353,] 7.6 0.645 0.03 1.90 0.086
## [1354,] 7.6 0.645 0.03 1.90 0.086
## [1355,] 7.2 0.580 0.03 2.30 0.077
## [1356,] 6.1 0.320 0.25 1.80 0.086
## [1357,] 6.1 0.340 0.25 1.80 0.084
## [1358,] 7.3 0.430 0.24 2.50 0.078
## [1359,] 7.4 0.640 0.17 5.40 0.168
## [1360,] 11.6 0.475 0.40 1.40 0.091
## [1361,] 9.2 0.540 0.31 2.30 0.112
## [1362,] 8.3 0.850 0.14 2.50 0.093
## [1363,] 11.6 0.475 0.40 1.40 0.091
## [1364,] 8.0 0.830 0.27 2.00 0.080
## [1365,] 7.2 0.605 0.02 1.90 0.096
## [1366,] 7.8 0.500 0.09 2.20 0.115
## [1367,] 7.3 0.740 0.08 1.70 0.094
## [1368,] 6.9 0.540 0.30 2.20 0.088
## [1369,] 8.0 0.770 0.32 2.10 0.079
## [1370,] 6.6 0.610 0.00 1.60 0.069
## [1371,] 8.7 0.780 0.51 1.70 0.415
## [1372,] 7.5 0.580 0.56 3.10 0.153
## [1373,] 8.7 0.780 0.51 1.70 0.415
## [1374,] 7.7 0.750 0.27 3.80 0.110
## [1375,] 6.8 0.815 0.00 1.20 0.267
## [1376,] 7.2 0.560 0.26 2.00 0.083
## [1377,] 8.2 0.885 0.20 1.40 0.086
## [1378,] 5.2 0.490 0.26 2.30 0.090
## [1379,] 7.2 0.450 0.15 2.00 0.078
## [1380,] 7.5 0.570 0.02 2.60 0.077
## [1381,] 7.5 0.570 0.02 2.60 0.077
## [1382,] 6.8 0.830 0.09 1.80 0.074
## [1383,] 8.0 0.600 0.22 2.10 0.080
## [1384,] 8.0 0.600 0.22 2.10 0.080
## [1385,] 7.1 0.755 0.15 1.80 0.107
## [1386,] 8.0 0.810 0.25 3.40 0.076
## [1387,] 7.4 0.640 0.07 1.80 0.100
## [1388,] 7.4 0.640 0.07 1.80 0.100
## [1389,] 6.6 0.640 0.31 6.10 0.083
## [1390,] 6.7 0.480 0.02 2.20 0.080
## [1391,] 6.0 0.490 0.00 2.30 0.068
## [1392,] 8.0 0.640 0.22 2.40 0.094
## [1393,] 7.1 0.620 0.06 1.30 0.070
## [1394,] 8.0 0.520 0.25 2.00 0.078
## [1395,] 6.4 0.570 0.14 3.90 0.070
## [1396,] 8.6 0.685 0.10 1.60 0.092
## [1397,] 8.7 0.675 0.10 1.60 0.090
## [1398,] 7.3 0.590 0.26 2.00 0.080
## [1399,] 7.0 0.600 0.12 2.20 0.083
## [1400,] 7.2 0.670 0.00 2.20 0.068
## [1401,] 7.9 0.690 0.21 2.10 0.080
## [1402,] 7.9 0.690 0.21 2.10 0.080
## [1403,] 7.6 0.300 0.42 2.00 0.052
## [1404,] 7.2 0.330 0.33 1.70 0.061
## [1405,] 8.0 0.500 0.39 2.60 0.082
## [1406,] 7.7 0.280 0.30 2.00 0.062
## [1407,] 8.2 0.240 0.34 5.10 0.062
## [1408,] 6.0 0.510 0.00 2.10 0.064
## [1409,] 8.1 0.290 0.36 2.20 0.048
## [1410,] 6.0 0.510 0.00 2.10 0.064
## [1411,] 6.6 0.960 0.00 1.80 0.082
## [1412,] 6.4 0.470 0.40 2.40 0.071
## [1413,] 8.2 0.240 0.34 5.10 0.062
## [1414,] 9.9 0.570 0.25 2.00 0.104
## [1415,] 10.0 0.320 0.59 2.20 0.077
## [1416,] 6.2 0.580 0.00 1.60 0.065
## [1417,] 10.0 0.320 0.59 2.20 0.077
## [1418,] 7.3 0.340 0.33 2.50 0.064
## [1419,] 7.8 0.530 0.01 1.60 0.077
## [1420,] 7.7 0.640 0.21 2.20 0.077
## [1421,] 7.8 0.530 0.01 1.60 0.077
## [1422,] 7.5 0.400 0.18 1.60 0.079
## [1423,] 7.0 0.540 0.00 2.10 0.079
## [1424,] 6.4 0.530 0.09 3.90 0.123
## [1425,] 8.3 0.260 0.37 1.40 0.076
## [1426,] 8.3 0.260 0.37 1.40 0.076
## [1427,] 7.7 0.230 0.37 1.80 0.046
## [1428,] 7.6 0.410 0.33 2.50 0.078
## [1429,] 7.8 0.640 0.00 1.90 0.072
## [1430,] 7.9 0.180 0.40 2.20 0.049
## [1431,] 7.4 0.410 0.24 1.80 0.066
## [1432,] 7.6 0.430 0.31 2.10 0.069
## [1433,] 5.9 0.440 0.00 1.60 0.042
## [1434,] 6.1 0.400 0.16 1.80 0.069
## [1435,] 10.2 0.540 0.37 15.40 0.214
## [1436,] 10.2 0.540 0.37 15.40 0.214
## [1437,] 10.0 0.380 0.38 1.60 0.169
## [1438,] 6.8 0.915 0.29 4.80 0.070
## [1439,] 7.0 0.590 0.00 1.70 0.052
## [1440,] 7.3 0.670 0.02 2.20 0.072
## [1441,] 7.2 0.370 0.32 2.00 0.062
## [1442,] 7.4 0.785 0.19 5.20 0.094
## [1443,] 6.9 0.630 0.02 1.90 0.078
## [1444,] 6.9 0.580 0.20 1.75 0.058
## [1445,] 7.3 0.670 0.02 2.20 0.072
## [1446,] 7.4 0.785 0.19 5.20 0.094
## [1447,] 6.9 0.630 0.02 1.90 0.078
## [1448,] 6.8 0.670 0.00 1.90 0.080
## [1449,] 6.9 0.580 0.01 1.90 0.080
## [1450,] 7.2 0.380 0.31 2.00 0.056
## [1451,] 7.2 0.370 0.32 2.00 0.062
## [1452,] 7.8 0.320 0.44 2.70 0.104
## [1453,] 6.6 0.580 0.02 2.00 0.062
## [1454,] 7.6 0.490 0.33 1.90 0.074
## [1455,] 11.7 0.450 0.63 2.20 0.073
## [1456,] 6.5 0.900 0.00 1.60 0.052
## [1457,] 6.0 0.540 0.06 1.80 0.050
## [1458,] 7.6 0.490 0.33 1.90 0.074
## [1459,] 8.4 0.290 0.40 1.70 0.067
## [1460,] 7.9 0.200 0.35 1.70 0.054
## [1461,] 6.4 0.420 0.09 2.30 0.054
## [1462,] 6.2 0.785 0.00 2.10 0.060
## [1463,] 6.8 0.640 0.03 2.30 0.075
## [1464,] 6.9 0.630 0.01 2.40 0.076
## [1465,] 6.8 0.590 0.10 1.70 0.063
## [1466,] 6.8 0.590 0.10 1.70 0.063
## [1467,] 7.3 0.480 0.32 2.10 0.062
## [1468,] 6.7 1.040 0.08 2.30 0.067
## [1469,] 7.3 0.480 0.32 2.10 0.062
## [1470,] 7.3 0.980 0.05 2.10 0.061
## [1471,] 10.0 0.690 0.11 1.40 0.084
## [1472,] 6.7 0.700 0.08 3.75 0.067
## [1473,] 7.6 0.350 0.60 2.60 0.073
## [1474,] 6.1 0.600 0.08 1.80 0.071
## [1475,] 9.9 0.500 0.50 13.80 0.205
## [1476,] 5.3 0.470 0.11 2.20 0.048
## [1477,] 9.9 0.500 0.50 13.80 0.205
## [1478,] 5.3 0.470 0.11 2.20 0.048
## [1479,] 7.1 0.875 0.05 5.70 0.082
## [1480,] 8.2 0.280 0.60 3.00 0.104
## [1481,] 5.6 0.620 0.03 1.50 0.080
## [1482,] 8.2 0.280 0.60 3.00 0.104
## [1483,] 7.2 0.580 0.54 2.10 0.114
## [1484,] 8.1 0.330 0.44 1.50 0.042
## [1485,] 6.8 0.910 0.06 2.00 0.060
## [1486,] 7.0 0.655 0.16 2.10 0.074
## [1487,] 6.8 0.680 0.21 2.10 0.070
## [1488,] 6.0 0.640 0.05 1.90 0.066
## [1489,] 5.6 0.540 0.04 1.70 0.049
## [1490,] 6.2 0.570 0.10 2.10 0.048
## [1491,] 7.1 0.220 0.49 1.80 0.039
## [1492,] 5.6 0.540 0.04 1.70 0.049
## [1493,] 6.2 0.650 0.06 1.60 0.050
## [1494,] 7.7 0.540 0.26 1.90 0.089
## [1495,] 6.4 0.310 0.09 1.40 0.066
## [1496,] 7.0 0.430 0.02 1.90 0.080
## [1497,] 7.7 0.540 0.26 1.90 0.089
## [1498,] 6.9 0.740 0.03 2.30 0.054
## [1499,] 6.6 0.895 0.04 2.30 0.068
## [1500,] 6.9 0.740 0.03 2.30 0.054
## [1501,] 7.5 0.725 0.04 1.50 0.076
## [1502,] 7.8 0.820 0.29 4.30 0.083
## [1503,] 7.3 0.585 0.18 2.40 0.078
## [1504,] 6.2 0.440 0.39 2.50 0.077
## [1505,] 7.5 0.380 0.57 2.30 0.106
## [1506,] 6.7 0.760 0.02 1.80 0.078
## [1507,] 6.8 0.810 0.05 2.00 0.070
## [1508,] 7.5 0.380 0.57 2.30 0.106
## [1509,] 7.1 0.270 0.60 2.10 0.074
## [1510,] 7.9 0.180 0.40 1.80 0.062
## [1511,] 6.4 0.360 0.21 2.20 0.047
## [1512,] 7.1 0.690 0.04 2.10 0.068
## [1513,] 6.4 0.790 0.04 2.20 0.061
## [1514,] 6.4 0.560 0.15 1.80 0.078
## [1515,] 6.9 0.840 0.21 4.10 0.074
## [1516,] 6.9 0.840 0.21 4.10 0.074
## [1517,] 6.1 0.320 0.25 2.30 0.071
## [1518,] 6.5 0.530 0.06 2.00 0.063
## [1519,] 7.4 0.470 0.46 2.20 0.114
## [1520,] 6.6 0.700 0.08 2.60 0.106
## [1521,] 6.5 0.530 0.06 2.00 0.063
## [1522,] 6.9 0.480 0.20 1.90 0.082
## [1523,] 6.1 0.320 0.25 2.30 0.071
## [1524,] 6.8 0.480 0.25 2.00 0.076
## [1525,] 6.0 0.420 0.19 2.00 0.075
## [1526,] 6.7 0.480 0.08 2.10 0.064
## [1527,] 6.8 0.470 0.08 2.20 0.064
## [1528,] 7.1 0.530 0.07 1.70 0.071
## [1529,] 7.9 0.290 0.49 2.20 0.096
## [1530,] 7.1 0.690 0.08 2.10 0.063
## [1531,] 6.6 0.440 0.09 2.20 0.063
## [1532,] 6.1 0.705 0.10 2.80 0.081
## [1533,] 7.2 0.530 0.13 2.00 0.058
## [1534,] 8.0 0.390 0.30 1.90 0.074
## [1535,] 6.6 0.560 0.14 2.40 0.064
## [1536,] 7.0 0.550 0.13 2.20 0.075
## [1537,] 6.1 0.530 0.08 1.90 0.077
## [1538,] 5.4 0.580 0.08 1.90 0.059
## [1539,] 6.2 0.640 0.09 2.50 0.081
## [1540,] 7.2 0.390 0.32 1.80 0.065
## [1541,] 6.2 0.520 0.08 4.40 0.071
## [1542,] 7.4 0.250 0.29 2.20 0.054
## [1543,] 6.7 0.855 0.02 1.90 0.064
## [1544,] 11.1 0.440 0.42 2.20 0.064
## [1545,] 8.4 0.370 0.43 2.30 0.063
## [1546,] 6.5 0.630 0.33 1.80 0.059
## [1547,] 7.0 0.570 0.02 2.00 0.072
## [1548,] 6.3 0.600 0.10 1.60 0.048
## [1549,] 11.2 0.400 0.50 2.00 0.099
## [1550,] 7.4 0.360 0.30 1.80 0.074
## [1551,] 7.1 0.680 0.00 2.30 0.087
## [1552,] 7.1 0.670 0.00 2.30 0.083
## [1553,] 6.3 0.680 0.01 3.70 0.103
## [1554,] 7.3 0.735 0.00 2.20 0.080
## [1555,] 6.6 0.855 0.02 2.40 0.062
## [1556,] 7.0 0.560 0.17 1.70 0.065
## [1557,] 6.6 0.880 0.04 2.20 0.066
## [1558,] 6.6 0.855 0.02 2.40 0.062
## [1559,] 6.9 0.630 0.33 6.70 0.235
## [1560,] 7.8 0.600 0.26 2.00 0.080
## [1561,] 7.8 0.600 0.26 2.00 0.080
## [1562,] 7.8 0.600 0.26 2.00 0.080
## [1563,] 7.2 0.695 0.13 2.00 0.076
## [1564,] 7.2 0.695 0.13 2.00 0.076
## [1565,] 7.2 0.695 0.13 2.00 0.076
## [1566,] 6.7 0.670 0.02 1.90 0.061
## [1567,] 6.7 0.160 0.64 2.10 0.059
## [1568,] 7.2 0.695 0.13 2.00 0.076
## [1569,] 7.0 0.560 0.13 1.60 0.077
## [1570,] 6.2 0.510 0.14 1.90 0.056
## [1571,] 6.4 0.360 0.53 2.20 0.230
## [1572,] 6.4 0.380 0.14 2.20 0.038
## [1573,] 7.3 0.690 0.32 2.20 0.069
## [1574,] 6.0 0.580 0.20 2.40 0.075
## [1575,] 5.6 0.310 0.78 13.90 0.074
## [1576,] 7.5 0.520 0.40 2.20 0.060
## [1577,] 8.0 0.300 0.63 1.60 0.081
## [1578,] 6.2 0.700 0.15 5.10 0.076
## [1579,] 6.8 0.670 0.15 1.80 0.118
## [1580,] 6.2 0.560 0.09 1.70 0.053
## [1581,] 7.4 0.350 0.33 2.40 0.068
## [1582,] 6.2 0.560 0.09 1.70 0.053
## [1583,] 6.1 0.715 0.10 2.60 0.053
## [1584,] 6.2 0.460 0.29 2.10 0.074
## [1585,] 6.7 0.320 0.44 2.40 0.061
## [1586,] 7.2 0.390 0.44 2.60 0.066
## [1587,] 7.5 0.310 0.41 2.40 0.065
## [1588,] 5.8 0.610 0.11 1.80 0.066
## [1589,] 7.2 0.660 0.33 2.50 0.068
## [1590,] 6.6 0.725 0.20 7.80 0.073
## [1591,] 6.3 0.550 0.15 1.80 0.077
## [1592,] 5.4 0.740 0.09 1.70 0.089
## [1593,] 6.3 0.510 0.13 2.30 0.076
## [1594,] 6.8 0.620 0.08 1.90 0.068
## [1595,] 6.2 0.600 0.08 2.00 0.090
## [1596,] 5.9 0.550 0.10 2.20 0.062
## [1597,] 6.3 0.510 0.13 2.30 0.076
## [1598,] 5.9 0.645 0.12 2.00 0.075
## [1599,] 6.0 0.310 0.47 3.60 0.067
## free sulfur dioxide total sulfur dioxide density pH sulphates
## [1,] 11.0 34.0 0.99780 3.51 0.56
## [2,] 25.0 67.0 0.99680 3.20 0.68
## [3,] 15.0 54.0 0.99700 3.26 0.65
## [4,] 17.0 60.0 0.99800 3.16 0.58
## [5,] 11.0 34.0 0.99780 3.51 0.56
## [6,] 13.0 40.0 0.99780 3.51 0.56
## [7,] 15.0 59.0 0.99640 3.30 0.46
## [8,] 15.0 21.0 0.99460 3.39 0.47
## [9,] 9.0 18.0 0.99680 3.36 0.57
## [10,] 17.0 102.0 0.99780 3.35 0.80
## [11,] 15.0 65.0 0.99590 3.28 0.54
## [12,] 17.0 102.0 0.99780 3.35 0.80
## [13,] 16.0 59.0 0.99430 3.58 0.52
## [14,] 9.0 29.0 0.99740 3.26 1.56
## [15,] 52.0 145.0 0.99860 3.16 0.88
## [16,] 51.0 148.0 0.99860 3.17 0.93
## [17,] 35.0 103.0 0.99690 3.30 0.75
## [18,] 16.0 56.0 0.99680 3.11 1.28
## [19,] 6.0 29.0 0.99740 3.38 0.50
## [20,] 17.0 56.0 0.99690 3.04 1.08
## [21,] 29.0 60.0 0.99680 3.39 0.53
## [22,] 23.0 71.0 0.99820 3.52 0.65
## [23,] 10.0 37.0 0.99660 3.17 0.91
## [24,] 9.0 67.0 0.99680 3.17 0.53
## [25,] 21.0 40.0 0.99680 3.43 0.63
## [26,] 11.0 23.0 0.99550 3.34 0.56
## [27,] 4.0 11.0 0.99620 3.28 0.59
## [28,] 10.0 37.0 0.99660 3.17 0.91
## [29,] 14.0 35.0 0.99720 3.47 0.55
## [30,] 8.0 16.0 0.99640 3.38 0.59
## [31,] 17.0 82.0 0.99580 3.35 0.54
## [32,] 22.0 37.0 0.99660 3.46 0.57
## [33,] 15.0 113.0 0.99660 3.17 0.66
## [34,] 40.0 83.0 0.99930 3.45 0.52
## [35,] 13.0 50.0 0.99570 3.38 0.55
## [36,] 5.0 18.0 0.99860 3.40 0.55
## [37,] 3.0 15.0 0.99750 3.42 0.60
## [38,] 13.0 30.0 0.99680 3.23 0.73
## [39,] 7.0 19.0 0.99400 3.50 0.48
## [40,] 12.0 87.0 0.99780 3.33 0.83
## [41,] 12.0 87.0 0.99780 3.33 0.83
## [42,] 17.0 46.0 0.99760 3.26 0.51
## [43,] 8.0 14.0 0.99680 3.21 0.90
## [44,] 9.0 23.0 0.99680 3.30 1.20
## [45,] 5.0 11.0 0.99620 3.48 0.52
## [46,] 8.0 65.0 0.99340 3.90 0.56
## [47,] 22.0 114.0 0.99700 3.25 0.73
## [48,] 12.0 37.0 0.99690 3.25 0.58
## [49,] 5.0 12.0 0.99580 3.34 0.56
## [50,] 12.0 96.0 0.99540 3.32 0.58
## [51,] 4.0 23.0 0.99710 3.15 0.74
## [52,] 8.0 15.0 0.99560 3.40 0.63
## [53,] 6.0 14.0 0.99550 3.39 0.64
## [54,] 30.0 119.0 0.99700 3.20 0.56
## [55,] 33.0 73.0 0.99550 3.17 0.63
## [56,] 25.0 45.0 0.99780 3.34 0.53
## [57,] 4.0 10.0 0.99710 3.04 0.63
## [58,] 50.0 110.0 0.99830 3.26 0.77
## [59,] 17.0 54.0 0.99750 3.43 0.59
## [60,] 9.0 46.0 0.99620 3.41 0.54
## [61,] 19.0 52.0 0.99800 3.44 0.64
## [62,] 20.0 112.0 0.99680 3.21 0.71
## [63,] 12.0 35.0 0.99680 3.38 0.62
## [64,] 13.0 54.0 0.99660 3.39 0.57
## [65,] 4.0 11.0 0.99620 3.41 0.39
## [66,] 4.0 11.0 0.99620 3.41 0.39
## [67,] 11.0 39.0 0.99680 3.42 0.58
## [68,] 6.0 15.0 0.99620 3.44 0.58
## [69,] 27.0 65.0 0.99690 3.28 0.79
## [70,] 8.0 19.0 0.99620 3.34 0.95
## [71,] 15.0 27.0 0.99670 3.32 0.54
## [72,] 17.0 96.0 0.99620 3.32 0.48
## [73,] 18.0 94.0 0.99610 3.31 0.48
## [74,] 11.0 43.0 0.99760 3.31 0.53
## [75,] 28.0 83.0 0.99840 3.28 0.82
## [76,] 9.0 42.0 0.99860 3.54 0.66
## [77,] 9.0 42.0 0.99860 3.54 0.66
## [78,] 14.0 30.0 0.99660 3.52 0.55
## [79,] 12.0 80.0 0.99580 3.38 0.52
## [80,] 27.0 119.0 0.99720 3.16 1.12
## [81,] 3.0 15.0 0.99580 3.41 0.56
## [82,] 22.0 67.0 0.99740 3.13 1.28
## [83,] 21.0 73.0 0.99700 3.36 0.57
## [84,] 16.0 51.0 0.99690 3.16 1.14
## [85,] 18.0 61.0 0.99590 3.44 0.78
## [86,] 19.0 40.0 0.99610 3.41 0.59
## [87,] 20.0 136.0 0.99720 2.93 1.95
## [88,] 9.0 31.0 0.99660 3.39 0.64
## [89,] 34.0 125.0 0.99780 3.14 1.22
## [90,] 8.0 24.0 0.99780 3.48 0.53
## [91,] 42.0 140.0 0.99640 3.23 0.54
## [92,] 20.0 136.0 0.99720 2.93 1.95
## [93,] 19.0 133.0 0.99720 2.93 1.98
## [94,] 9.0 31.0 0.99660 3.39 0.64
## [95,] 41.0 85.0 0.99380 3.75 0.48
## [96,] 17.0 106.0 0.99320 3.85 0.60
## [97,] 8.0 23.0 0.99650 3.45 0.56
## [98,] 3.0 22.0 0.99630 3.25 0.63
## [99,] 5.0 10.0 0.99670 3.39 0.56
## [100,] 13.0 35.0 0.99720 3.30 0.59
## [101,] 11.0 50.0 0.99720 3.40 0.61
## [102,] 8.0 22.0 0.99590 3.31 0.56
## [103,] 13.0 35.0 0.99720 3.30 0.59
## [104,] 12.0 65.0 0.99670 3.29 0.51
## [105,] 5.0 36.0 0.99600 3.33 0.48
## [106,] 12.0 65.0 0.99670 3.29 0.51
## [107,] 18.0 69.0 0.99730 3.08 1.31
## [108,] 15.0 64.0 0.99690 3.46 0.79
## [109,] 18.0 80.0 0.99760 3.37 0.80
## [110,] 37.0 153.0 0.99690 3.21 0.69
## [111,] 12.0 47.0 0.99640 3.19 0.93
## [112,] 11.0 108.0 0.99640 3.15 0.66
## [113,] 14.0 111.0 0.99640 3.15 0.66
## [114,] 22.0 46.0 0.99880 3.32 0.67
## [115,] 12.0 47.0 0.99640 3.19 0.93
## [116,] 13.0 62.0 0.99660 3.07 0.63
## [117,] 11.0 40.0 0.99780 3.39 0.61
## [118,] 7.0 28.0 0.99700 3.37 0.50
## [119,] 14.0 56.0 0.99620 3.21 0.60
## [120,] 22.0 89.0 0.99590 3.34 0.54
## [121,] 10.0 89.0 0.99620 3.30 0.57
## [122,] 14.0 56.0 0.99620 3.21 0.60
## [123,] 3.0 13.0 0.99800 3.49 0.52
## [124,] 11.0 34.0 0.99760 3.44 0.53
## [125,] 21.0 102.0 0.99600 3.39 0.48
## [126,] 27.0 90.0 0.99840 3.16 0.70
## [127,] 3.0 12.0 0.99640 3.53 0.49
## [128,] 3.0 12.0 0.99640 3.54 0.48
## [129,] 3.0 16.0 0.99620 3.42 0.92
## [130,] 6.0 19.0 0.99550 3.42 0.57
## [131,] 30.0 134.0 0.99680 3.24 0.66
## [132,] 17.0 99.0 0.99370 3.63 0.63
## [133,] 17.0 99.0 0.99370 3.63 0.63
## [134,] 17.0 26.0 0.99520 3.40 0.58
## [135,] 13.0 29.0 0.99590 3.22 0.55
## [136,] 16.0 63.0 0.99650 3.19 0.82
## [137,] 10.0 52.0 0.99680 3.23 0.77
## [138,] 13.0 45.0 0.99720 3.48 0.64
## [139,] 15.0 105.0 0.99620 3.33 0.54
## [140,] 17.0 108.0 0.99620 3.32 0.54
## [141,] 16.0 63.0 0.99650 3.19 0.82
## [142,] 10.0 52.0 0.99680 3.23 0.77
## [143,] 27.0 63.0 0.99160 3.68 0.79
## [144,] 3.0 20.0 0.99540 3.34 0.58
## [145,] 27.0 63.0 0.99160 3.68 0.79
## [146,] 32.0 141.0 0.99680 3.17 0.62
## [147,] 21.0 94.0 0.99440 3.54 0.52
## [148,] 10.0 88.0 0.99680 3.11 0.80
## [149,] 12.0 30.0 0.99590 3.42 0.58
## [150,] 11.0 43.0 0.99750 3.53 0.61
## [151,] 5.0 11.0 0.99580 3.33 0.53
## [152,] 32.0 69.0 0.99960 2.74 2.00
## [153,] 25.0 99.0 0.99500 3.35 0.54
## [154,] 25.0 99.0 0.99500 3.35 0.54
## [155,] 29.0 129.0 0.99730 3.42 0.72
## [156,] 28.0 128.0 0.99730 3.42 0.71
## [157,] 29.0 129.0 0.99730 3.42 0.72
## [158,] 28.0 128.0 0.99730 3.42 0.71
## [159,] 12.0 22.0 0.99690 3.48 0.50
## [160,] 18.0 86.0 0.99680 3.59 0.57
## [161,] 7.0 20.0 0.99590 3.20 0.56
## [162,] 9.0 20.0 0.99650 3.17 1.08
## [163,] 17.0 31.0 0.99640 3.33 0.56
## [164,] 36.0 121.0 0.99820 3.37 0.49
## [165,] 35.0 121.0 0.99810 3.37 0.49
## [166,] 14.0 96.0 0.99610 3.19 0.62
## [167,] 18.0 101.0 0.99560 3.34 0.52
## [168,] 17.0 42.0 0.99560 3.37 0.48
## [169,] 11.0 44.0 0.99530 3.47 0.55
## [170,] 15.0 63.0 0.99640 3.00 1.59
## [171,] 4.0 8.0 0.99720 3.36 0.33
## [172,] 6.0 18.0 0.99720 3.29 0.61
## [173,] 6.0 18.0 0.99720 3.29 0.61
## [174,] 24.0 42.0 0.99610 3.42 0.57
## [175,] 7.0 35.0 0.99610 3.33 0.47
## [176,] 19.0 49.0 0.99580 3.35 0.78
## [177,] 7.0 35.0 0.99610 3.33 0.47
## [178,] 8.0 38.0 0.99720 3.58 0.61
## [179,] 7.0 20.0 0.99690 3.48 0.56
## [180,] 10.0 42.0 0.99690 3.19 0.59
## [181,] 10.0 42.0 0.99690 3.19 0.59
## [182,] 23.0 110.0 0.99720 3.12 1.02
## [183,] 16.0 42.0 0.99720 3.44 0.52
## [184,] 11.0 65.0 0.99710 3.54 0.58
## [185,] 8.0 62.0 0.99700 3.52 0.58
## [186,] 26.0 85.0 0.99710 3.26 0.53
## [187,] 14.0 67.0 0.99720 3.34 0.55
## [188,] 9.0 26.0 0.99760 3.39 0.49
## [189,] 15.0 143.0 0.99680 3.20 0.55
## [190,] 17.0 144.0 0.99680 3.20 0.55
## [191,] 21.0 127.0 0.99760 3.23 0.62
## [192,] 21.0 49.0 0.99740 3.57 0.62
## [193,] 16.0 126.0 0.99690 3.28 0.61
## [194,] 7.0 28.0 0.99640 3.28 0.55
## [195,] 7.0 28.0 0.99640 3.28 0.55
## [196,] 24.0 120.0 0.99680 3.25 0.54
## [197,] 15.0 55.0 0.99680 3.46 0.59
## [198,] 12.0 27.0 0.99810 3.11 0.97
## [199,] 13.0 93.0 0.99240 3.57 0.85
## [200,] 12.0 31.0 0.99480 3.51 0.43
## [201,] 9.0 24.0 0.99695 3.22 0.82
## [202,] 39.0 145.0 0.99750 3.04 1.03
## [203,] 16.0 49.0 0.99545 3.36 0.79
## [204,] 16.0 39.0 0.99610 3.34 0.55
## [205,] 14.0 37.0 0.99615 3.34 0.56
## [206,] 9.0 28.0 0.99940 3.20 0.77
## [207,] 9.0 28.0 0.99940 3.20 0.77
## [208,] 26.0 120.0 0.99625 3.29 0.53
## [209,] 18.0 95.0 0.99660 3.22 0.67
## [210,] 7.0 19.0 0.99800 3.31 0.88
## [211,] 5.0 19.0 0.99585 3.30 0.86
## [212,] 10.0 62.0 0.99685 3.35 0.56
## [213,] 5.0 15.0 0.99800 3.21 0.67
## [214,] 28.0 65.0 0.99590 3.30 0.43
## [215,] 10.0 41.0 0.99740 3.24 0.71
## [216,] 26.0 121.0 0.99740 3.34 0.76
## [217,] 13.0 49.0 0.99620 3.14 0.57
## [218,] 11.0 41.0 0.99670 3.36 0.55
## [219,] 10.0 44.0 0.99570 3.39 0.54
## [220,] 24.0 144.0 0.99655 3.30 0.60
## [221,] 24.0 58.0 0.99640 3.34 0.59
## [222,] 16.0 72.0 0.99570 3.15 0.57
## [223,] 5.0 10.0 0.99525 3.42 0.60
## [224,] 8.0 28.0 0.99815 3.28 0.60
## [225,] 15.0 55.0 0.99745 3.31 0.57
## [226,] 29.0 63.0 0.99615 3.37 0.58
## [227,] 27.0 81.0 0.99640 3.04 1.61
## [228,] 9.0 23.0 0.99840 3.39 0.63
## [229,] 29.0 63.0 0.99615 3.37 0.58
## [230,] 10.0 37.0 0.99685 3.46 0.50
## [231,] 19.0 106.0 0.99270 3.54 0.62
## [232,] 12.0 49.0 0.99625 3.37 0.52
## [233,] 18.0 58.0 0.99800 3.34 0.70
## [234,] 10.0 37.0 0.99685 3.46 0.50
## [235,] 7.0 37.0 0.99685 3.32 0.55
## [236,] 14.0 38.0 0.99675 3.37 0.58
## [237,] 14.0 38.0 0.99675 3.37 0.58
## [238,] 15.0 39.0 0.99675 3.37 0.58
## [239,] 14.0 38.0 0.99675 3.37 0.58
## [240,] 7.0 37.0 0.99685 3.32 0.55
## [241,] 5.0 62.0 0.99710 3.00 1.09
## [242,] 6.0 24.0 0.99925 3.14 0.71
## [243,] 28.0 109.0 0.99565 3.08 0.49
## [244,] 10.0 24.0 1.00005 3.07 0.84
## [245,] 10.0 24.0 1.00005 3.07 0.84
## [246,] 6.0 23.0 0.99830 3.61 0.96
## [247,] 16.0 51.0 0.99685 3.38 0.52
## [248,] 11.0 73.0 0.99630 3.20 0.45
## [249,] 9.0 39.0 0.99615 3.35 0.48
## [250,] 6.0 23.0 0.99830 3.61 0.96
## [251,] 16.0 37.0 0.99850 3.22 0.78
## [252,] 16.0 34.0 0.99720 3.47 0.70
## [253,] 5.0 21.0 0.99860 3.17 0.53
## [254,] 8.0 86.0 0.99590 3.23 0.59
## [255,] 16.0 34.0 0.99720 3.47 0.70
## [256,] 17.0 119.0 0.99675 3.26 0.57
## [257,] 5.0 13.0 0.99800 3.22 0.62
## [258,] 12.0 56.0 0.99680 3.49 0.67
## [259,] 8.0 45.0 0.99680 3.06 1.26
## [260,] 14.0 33.0 0.99965 3.36 0.80
## [261,] 18.0 45.0 0.99625 3.29 0.65
## [262,] 12.0 67.0 0.99565 3.35 0.60
## [263,] 10.0 35.0 0.99575 3.34 0.57
## [264,] 23.0 49.0 0.99630 3.28 0.67
## [265,] 5.0 27.0 0.99990 3.08 0.87
## [266,] 6.0 10.0 0.99680 3.20 0.72
## [267,] 26.0 61.0 1.00025 3.60 0.72
## [268,] 15.0 37.0 0.99730 3.35 0.86
## [269,] 7.0 27.0 0.99870 3.69 0.91
## [270,] 4.0 23.0 0.99960 3.28 0.97
## [271,] 27.0 61.0 0.99650 3.36 0.67
## [272,] 4.0 23.0 0.99960 3.28 0.97
## [273,] 17.0 65.0 0.99935 3.22 0.78
## [274,] 10.0 38.0 0.99735 3.31 0.64
## [275,] 27.0 94.0 0.99915 3.38 0.77
## [276,] 27.0 61.0 0.99650 3.36 0.67
## [277,] 7.0 27.0 0.99870 3.69 0.91
## [278,] 4.0 23.0 0.99960 3.28 0.97
## [279,] 5.0 13.0 0.99760 3.23 0.82
## [280,] 10.0 47.0 0.99910 3.38 0.77
## [281,] 6.0 19.0 0.99860 3.12 0.82
## [282,] 5.0 10.0 0.99720 3.25 1.08
## [283,] 12.0 53.0 0.99710 3.36 0.57
## [284,] 10.0 47.0 0.99910 3.38 0.77
## [285,] 32.0 71.0 1.00015 3.31 0.71
## [286,] 32.0 71.0 1.00015 3.31 0.71
## [287,] 25.0 49.0 0.99970 3.10 0.76
## [288,] 29.0 53.0 0.99670 3.37 0.70
## [289,] 20.0 49.0 0.99760 3.34 0.86
## [290,] 33.0 98.0 1.00100 3.20 0.95
## [291,] 20.0 49.0 0.99760 3.34 0.86
## [292,] 6.0 18.0 0.99790 3.30 0.71
## [293,] 18.0 48.0 0.99940 3.22 0.64
## [294,] 5.0 16.0 0.99640 3.41 0.60
## [295,] 17.0 53.0 1.00140 3.05 0.81
## [296,] 5.0 27.0 1.00010 3.05 0.64
## [297,] 26.0 70.0 0.99810 3.16 0.52
## [298,] 8.0 29.0 0.99855 3.67 0.73
## [299,] 5.0 31.0 0.99930 3.67 0.80
## [300,] 8.0 33.0 0.99845 3.68 0.71
## [301,] 20.0 44.0 0.99650 3.38 0.59
## [302,] 7.0 15.0 0.99730 3.22 0.64
## [303,] 12.0 28.0 0.99940 3.51 0.72
## [304,] 5.0 21.0 0.99600 3.39 0.54
## [305,] 12.0 90.0 0.99730 3.20 0.52
## [306,] 6.0 25.0 0.99980 3.12 0.59
## [307,] 7.0 54.0 0.99660 3.36 0.52
## [308,] 6.0 14.0 0.99940 3.19 0.62
## [309,] 5.0 12.0 0.99940 3.19 0.63
## [310,] 9.0 30.0 0.99680 3.41 0.53
## [311,] 6.0 25.0 0.99980 3.12 0.59
## [312,] 15.0 105.0 0.99600 3.27 0.54
## [313,] 19.0 98.0 0.99815 3.32 0.63
## [314,] 30.0 135.0 0.99760 3.30 0.53
## [315,] 26.0 72.0 0.99645 3.39 0.68
## [316,] 20.0 53.0 0.99620 3.42 0.65
## [317,] 37.0 92.0 0.99960 3.30 0.65
## [318,] 30.0 74.0 0.99865 3.30 0.64
## [319,] 21.0 59.0 0.99890 3.37 0.71
## [320,] 30.0 74.0 0.99865 3.30 0.64
## [321,] 21.0 59.0 0.99890 3.37 0.71
## [322,] 25.0 87.0 0.99975 3.24 0.62
## [323,] 6.0 18.0 0.99735 3.29 0.63
## [324,] 35.0 73.0 0.99900 3.28 0.70
## [325,] 13.0 50.0 1.00150 3.16 0.69
## [326,] 13.0 50.0 1.00150 3.16 0.69
## [327,] 6.0 14.0 0.99780 3.05 0.74
## [328,] 5.0 13.0 0.99800 3.28 0.83
## [329,] 6.0 21.0 1.00020 3.16 0.67
## [330,] 7.0 15.0 0.99810 3.18 0.62
## [331,] 10.0 41.0 0.99800 3.23 0.66
## [332,] 10.0 41.0 0.99800 3.23 0.66
## [333,] 21.0 114.0 0.99730 3.22 0.54
## [334,] 16.0 44.0 0.99580 3.13 0.52
## [335,] 17.0 38.0 0.99630 3.34 0.74
## [336,] 7.0 21.0 0.99920 3.17 0.84
## [337,] 6.0 16.0 0.99480 3.35 0.70
## [338,] 29.0 58.0 0.99740 3.31 0.64
## [339,] 28.0 99.0 1.00080 3.16 1.00
## [340,] 12.0 29.0 0.99970 3.11 1.36
## [341,] 10.0 21.0 1.00000 3.12 1.18
## [342,] 5.0 18.0 0.99720 3.21 0.87
## [343,] 6.0 14.0 0.99820 3.30 0.75
## [344,] 6.0 14.0 0.99820 3.30 0.75
## [345,] 6.0 32.0 1.00060 3.12 0.78
## [346,] 40.0 63.0 0.99790 3.60 0.81
## [347,] 17.0 34.0 0.99550 3.58 0.89
## [348,] 6.0 15.0 0.99860 3.02 0.93
## [349,] 15.0 46.0 0.99790 3.11 0.92
## [350,] 11.0 28.0 0.99940 3.36 0.86
## [351,] 17.0 34.0 1.00040 3.28 0.98
## [352,] 11.0 26.0 0.99940 3.35 0.83
## [353,] 8.0 19.0 0.99740 3.27 0.73
## [354,] 23.0 77.0 1.00180 3.18 0.77
## [355,] 40.5 165.0 0.99120 3.25 0.59
## [356,] 17.0 32.0 0.99550 3.55 0.61
## [357,] 29.0 55.0 1.00010 3.26 0.88
## [358,] 12.0 29.0 0.99700 3.24 0.75
## [359,] 10.0 23.0 1.00000 3.15 0.85
## [360,] 10.0 41.0 1.00100 3.17 0.68
## [361,] 14.0 81.0 0.99730 3.19 0.70
## [362,] 21.0 50.0 0.99820 3.37 0.91
## [363,] 6.0 37.0 0.99920 3.05 0.56
## [364,] 6.0 15.0 0.99880 2.99 0.87
## [365,] 7.0 42.0 1.00220 3.07 0.73
## [366,] 7.0 21.0 0.99790 3.26 0.93
## [367,] 7.0 42.0 1.00220 3.07 0.73
## [368,] 11.0 24.0 1.00000 3.16 0.69
## [369,] 15.0 75.0 0.99980 3.18 0.64
## [370,] 6.0 18.0 0.99620 3.20 1.13
## [371,] 35.0 63.0 0.99750 3.57 0.78
## [372,] 11.0 25.0 0.99670 3.32 0.87
## [373,] 26.0 46.0 0.99670 3.32 1.04
## [374,] 12.0 72.0 0.99590 3.05 0.63
## [375,] 6.0 47.0 1.00140 3.01 0.81
## [376,] 6.0 15.0 0.99840 3.01 0.83
## [377,] 19.0 47.0 1.00030 3.26 1.11
## [378,] 6.0 18.0 0.99620 3.20 1.13
## [379,] 6.0 24.0 0.99880 3.11 0.99
## [380,] 24.0 60.0 0.99790 3.31 0.70
## [381,] 11.0 27.0 0.99740 3.21 0.80
## [382,] 17.0 43.0 1.00140 3.06 0.80
## [383,] 11.0 27.0 0.99740 3.21 0.80
## [384,] 11.0 27.0 0.99740 3.21 0.80
## [385,] 23.0 54.0 0.99800 3.42 0.74
## [386,] 11.0 37.0 0.99790 3.43 0.76
## [387,] 23.0 48.0 0.99810 3.41 0.74
## [388,] 17.0 42.0 0.99720 3.31 0.54
## [389,] 23.0 53.0 0.99810 3.43 0.74
## [390,] 16.0 49.0 0.99820 3.19 0.70
## [391,] 12.0 88.0 0.99240 3.56 0.82
## [392,] 17.0 43.0 1.00140 3.06 0.80
## [393,] 6.0 26.0 0.99800 3.18 0.51
## [394,] 13.0 95.0 0.99700 3.16 0.54
## [395,] 6.0 25.0 0.99970 3.03 0.57
## [396,] 7.0 38.0 1.00040 3.22 0.60
## [397,] 68.0 124.0 0.99940 3.47 0.53
## [398,] 13.0 49.0 0.99880 3.18 0.65
## [399,] 13.0 49.0 0.99880 3.18 0.65
## [400,] 9.0 42.0 0.99630 3.10 0.55
## [401,] 68.0 124.0 0.99940 3.47 0.53
## [402,] 20.0 38.0 0.99490 3.29 0.47
## [403,] 19.0 64.0 1.00000 3.10 0.61
## [404,] 10.0 41.0 0.99940 3.15 0.63
## [405,] 15.0 27.0 0.99740 3.26 0.61
## [406,] 11.0 25.0 0.99660 3.36 0.76
## [407,] 9.0 28.0 0.99810 3.24 0.65
## [408,] 12.0 30.0 0.99960 3.18 0.63
## [409,] 6.0 16.0 0.99700 3.19 0.70
## [410,] 26.0 49.0 0.99810 3.05 0.57
## [411,] 26.0 86.0 0.99870 3.38 0.62
## [412,] 23.0 78.0 0.99870 3.38 0.62
## [413,] 15.0 77.0 0.99660 3.27 0.64
## [414,] 6.0 19.0 0.99860 3.27 0.82
## [415,] 24.0 122.0 0.99820 3.26 0.61
## [416,] 31.0 134.0 1.00140 3.32 1.07
## [417,] 6.0 20.0 0.99700 3.26 0.66
## [418,] 34.0 124.0 0.99560 3.44 0.48
## [419,] 7.0 20.0 0.99960 3.24 0.76
## [420,] 34.0 52.0 0.99760 3.62 0.68
## [421,] 35.0 67.0 0.99720 3.28 0.73
## [422,] 32.0 48.0 0.99520 3.52 0.56
## [423,] 15.0 60.0 0.99550 3.36 0.44
## [424,] 6.0 24.0 0.99780 3.15 0.90
## [425,] 15.0 60.0 0.99550 3.36 0.44
## [426,] 32.0 48.0 0.99520 3.52 0.56
## [427,] 19.0 48.0 0.99490 3.49 0.49
## [428,] 6.0 32.0 0.99880 3.26 0.56
## [429,] 9.0 30.0 0.99780 3.24 0.60
## [430,] 13.0 35.0 0.99970 3.10 0.60
## [431,] 6.0 24.0 0.99780 3.15 0.90
## [432,] 21.0 66.0 0.99740 3.25 0.56
## [433,] 12.0 24.0 0.99580 3.00 0.65
## [434,] 6.0 18.0 1.00040 3.16 0.49
## [435,] 22.0 54.0 0.99960 3.15 0.89
## [436,] 6.0 18.0 1.00040 3.16 0.49
## [437,] 38.0 62.0 0.99580 3.26 0.56
## [438,] 6.0 22.0 0.99860 3.17 0.66
## [439,] 22.0 54.0 0.99960 3.15 0.89
## [440,] 7.0 50.0 0.99510 3.08 0.60
## [441,] 6.0 29.0 0.99870 2.88 0.82
## [442,] 7.0 27.0 0.99880 3.06 0.68
## [443,] 6.0 43.0 1.00320 2.95 0.68
## [444,] 11.0 19.0 0.99630 3.23 0.63
## [445,] 5.0 27.0 0.99340 3.57 0.84
## [446,] 6.0 31.0 0.99860 3.23 0.56
## [447,] 31.0 72.0 0.99960 3.10 0.73
## [448,] 6.0 16.0 0.99680 3.22 0.72
## [449,] 7.0 27.0 0.99670 3.20 0.56
## [450,] 17.0 35.0 0.99940 3.10 0.61
## [451,] 17.0 35.0 0.99940 3.10 0.61
## [452,] 9.0 26.0 0.99790 3.06 1.06
## [453,] 18.0 35.0 0.99680 3.44 0.63
## [454,] 5.0 22.0 0.99980 3.26 0.74
## [455,] 21.0 67.0 0.99520 3.50 0.63
## [456,] 6.0 19.0 0.99880 3.22 0.69
## [457,] 5.0 22.0 0.99860 3.37 0.58
## [458,] 29.0 65.0 0.99880 3.28 0.58
## [459,] 5.0 22.0 0.99980 3.26 0.74
## [460,] 10.0 47.0 1.00080 3.25 0.57
## [461,] 14.0 23.0 0.99760 3.35 0.61
## [462,] 6.0 19.0 0.99820 3.26 0.61
## [463,] 10.0 25.0 0.99700 3.18 0.61
## [464,] 25.0 129.0 0.99720 3.08 0.53
## [465,] 5.0 15.0 0.99870 2.98 0.70
## [466,] 10.0 26.0 1.00060 3.48 0.91
## [467,] 21.0 51.0 0.99820 3.16 0.72
## [468,] 7.0 18.0 0.99470 3.32 0.79
## [469,] 6.0 21.0 1.00000 3.17 0.62
## [470,] 36.0 48.0 0.99520 3.20 0.58
## [471,] 15.0 47.0 0.99960 3.05 0.61
## [472,] 25.0 52.0 0.99700 3.20 0.71
## [473,] 25.0 68.0 0.99950 3.15 0.82
## [474,] 5.0 14.0 0.99710 3.26 0.79
## [475,] 10.0 24.0 0.99820 3.20 0.89
## [476,] 5.0 28.0 0.99880 3.14 0.60
## [477,] 6.0 16.0 0.99800 3.28 0.70
## [478,] 6.0 20.0 0.99770 3.18 1.06
## [479,] 5.0 28.0 0.99880 3.14 0.60
## [480,] 6.0 31.0 0.99840 3.19 0.70
## [481,] 6.0 23.0 1.00260 3.12 0.66
## [482,] 6.0 17.0 0.99640 3.15 0.92
## [483,] 6.0 18.0 0.99860 3.04 1.05
## [484,] 7.0 18.0 0.99860 3.04 1.06
## [485,] 6.0 24.0 0.99700 3.06 0.66
## [486,] 6.0 17.0 0.99760 3.17 0.47
## [487,] 6.0 17.0 0.99760 3.17 0.47
## [488,] 5.0 14.0 0.99820 3.17 0.42
## [489,] 35.0 67.0 1.00020 3.32 0.92
## [490,] 10.0 26.0 0.99840 3.34 0.75
## [491,] 24.0 56.0 0.99840 3.31 0.67
## [492,] 12.0 25.0 0.99520 3.34 0.79
## [493,] 13.0 27.0 0.99500 3.32 0.90
## [494,] 23.0 81.0 1.00020 3.48 0.74
## [495,] 28.0 91.0 0.99520 3.44 0.55
## [496,] 5.0 16.0 0.99720 3.15 0.65
## [497,] 14.0 38.0 0.99840 3.43 0.65
## [498,] 43.0 113.0 0.99660 3.32 0.79
## [499,] 5.0 16.0 0.99720 3.15 0.65
## [500,] 23.0 81.0 1.00020 3.48 0.74
## [501,] 14.0 38.0 0.99840 3.43 0.65
## [502,] 38.0 76.0 0.99900 3.17 0.85
## [503,] 38.0 76.0 0.99900 3.17 0.85
## [504,] 6.0 24.0 0.99760 3.18 1.04
## [505,] 6.0 22.0 0.99760 3.21 1.05
## [506,] 10.0 26.0 0.99680 3.16 0.78
## [507,] 6.0 21.0 0.99760 3.25 1.02
## [508,] 6.0 13.0 1.00000 3.17 0.71
## [509,] 26.0 62.0 0.99940 3.18 0.63
## [510,] 23.0 43.0 0.99860 3.04 0.68
## [511,] 19.0 43.0 0.99780 3.02 0.61
## [512,] 26.0 62.0 0.99940 3.18 0.63
## [513,] 15.0 49.0 0.99800 3.03 0.81
## [514,] 6.0 15.0 0.99730 3.09 0.66
## [515,] 6.0 15.0 0.99730 3.09 0.66
## [516,] 34.0 151.0 1.00100 3.31 1.14
## [517,] 5.0 14.0 1.00100 3.25 0.74
## [518,] 5.0 16.0 0.99940 3.16 0.63
## [519,] 11.0 32.0 0.99720 3.22 0.68
## [520,] 39.0 106.0 0.99660 3.36 0.78
## [521,] 15.0 33.0 0.99820 3.42 0.90
## [522,] 16.0 43.0 0.99800 3.48 0.64
## [523,] 47.0 133.0 0.99790 3.38 0.99
## [524,] 38.0 142.0 0.99780 3.22 0.55
## [525,] 23.0 116.0 0.99760 3.23 0.64
## [526,] 29.0 53.0 0.99980 3.24 0.67
## [527,] 39.0 106.0 0.99660 3.36 0.78
## [528,] 33.0 85.0 0.99620 3.39 0.77
## [529,] 32.0 55.0 0.99880 3.34 0.75
## [530,] 6.0 33.0 0.99740 3.09 0.57
## [531,] 1.0 28.0 0.99900 3.41 0.87
## [532,] 12.0 42.0 1.00040 3.16 0.61
## [533,] 12.0 42.0 1.00040 3.16 0.61
## [534,] 15.0 33.0 0.99560 3.22 0.66
## [535,] 13.0 32.0 1.00000 3.28 0.56
## [536,] 1.0 28.0 0.99900 3.41 0.87
## [537,] 6.0 33.0 0.99740 3.09 0.57
## [538,] 5.0 13.0 0.99720 3.37 0.77
## [539,] 5.0 35.0 1.00140 3.20 0.66
## [540,] 5.0 17.0 0.99960 3.22 0.62
## [541,] 20.0 47.0 0.99880 3.26 0.67
## [542,] 5.0 17.0 0.99880 3.21 0.73
## [543,] 5.0 20.0 0.99660 3.12 0.59
## [544,] 16.0 53.0 0.99930 3.06 0.72
## [545,] 6.0 15.0 1.00080 2.86 0.79
## [546,] 38.0 106.0 0.99820 3.08 0.59
## [547,] 10.0 28.0 0.99830 3.45 0.78
## [548,] 6.0 21.0 0.99870 3.26 0.86
## [549,] 27.0 69.0 0.99940 3.12 0.75
## [550,] 6.0 25.0 0.99750 3.27 0.61
## [551,] 9.0 25.0 0.99580 3.33 0.56
## [552,] 14.0 45.0 0.99800 3.19 0.73
## [553,] 14.0 44.0 0.99800 3.12 0.74
## [554,] 32.0 96.0 0.99340 3.74 0.62
## [555,] 10.0 23.0 1.00315 2.92 0.74
## [556,] 10.0 23.0 1.00315 2.92 0.74
## [557,] 10.0 17.0 1.00020 3.07 0.56
## [558,] 10.0 23.0 1.00315 2.92 0.74
## [559,] 10.0 17.0 1.00020 3.07 0.56
## [560,] 6.0 47.0 1.00210 3.30 0.68
## [561,] 5.0 19.0 0.99940 3.14 0.57
## [562,] 26.0 121.0 0.99780 3.23 0.58
## [563,] 41.0 110.0 0.99820 3.08 0.61
## [564,] 25.0 60.0 0.99710 3.31 0.61
## [565,] 6.0 47.0 1.00210 3.30 0.68
## [566,] 5.0 19.0 0.99940 3.14 0.57
## [567,] 5.0 15.0 0.99910 3.32 0.60
## [568,] 5.0 15.0 0.99910 3.32 0.60
## [569,] 5.0 20.0 0.99900 3.31 0.79
## [570,] 19.0 42.0 0.99460 3.57 0.57
## [571,] 10.0 37.0 1.00030 3.32 0.91
## [572,] 19.0 42.0 0.99460 3.57 0.57
## [573,] 10.0 28.0 0.99780 3.14 0.61
## [574,] 14.0 47.0 0.99910 3.30 0.56
## [575,] 20.0 78.0 0.99920 3.19 0.70
## [576,] 28.0 46.0 0.99930 3.20 0.80
## [577,] 6.0 14.0 0.99780 3.34 0.52
## [578,] 18.0 111.0 0.99820 3.30 0.60
## [579,] 17.0 110.0 0.99820 3.29 0.60
## [580,] 18.0 40.0 0.99760 3.14 0.51
## [581,] 5.0 14.0 1.00020 3.19 0.44
## [582,] 5.0 14.0 1.00020 3.19 0.44
## [583,] 5.0 15.0 1.00000 3.19 0.43
## [584,] 10.0 21.0 0.99760 2.98 0.66
## [585,] 54.0 80.0 1.00020 3.30 0.76
## [586,] 8.0 38.0 0.99800 3.47 0.66
## [587,] 16.0 47.0 0.99860 3.12 1.02
## [588,] 18.0 102.0 0.99670 3.26 0.59
## [589,] 19.0 50.0 0.99170 3.72 0.74
## [590,] 5.0 13.0 0.99760 3.05 0.74
## [591,] 21.0 75.0 0.99870 3.35 0.57
## [592,] 23.0 149.0 0.99220 3.12 0.50
## [593,] 21.0 75.0 0.99870 3.35 0.57
## [594,] 9.0 43.0 1.00040 3.29 0.58
## [595,] 20.0 38.0 0.99780 3.40 0.53
## [596,] 30.0 109.0 0.99980 3.27 0.57
## [597,] 6.0 24.0 0.99940 3.04 0.60
## [598,] 5.0 18.0 0.99800 3.09 0.63
## [599,] 5.0 30.0 0.99670 3.20 0.48
## [600,] 11.0 22.0 1.00000 3.00 0.70
## [601,] 7.0 23.0 0.99620 3.26 0.47
## [602,] 12.0 35.0 1.00060 3.10 0.56
## [603,] 6.0 14.0 0.99750 3.30 0.52
## [604,] 12.0 35.0 1.00060 3.10 0.56
## [605,] 14.0 63.0 0.99840 3.17 0.62
## [606,] 6.0 24.0 0.99840 3.31 0.59
## [607,] 10.0 20.0 0.99730 3.34 0.79
## [608,] 26.0 52.0 0.99820 3.31 0.53
## [609,] 11.0 65.0 1.00260 3.32 0.64
## [610,] 15.0 39.0 0.99560 3.56 0.52
## [611,] 25.0 57.0 0.99830 3.39 0.54
## [612,] 5.0 16.0 1.00060 2.98 0.54
## [613,] 18.0 29.0 0.99650 3.32 0.60
## [614,] 10.0 29.0 0.99620 3.26 0.74
## [615,] 10.0 103.0 0.99690 2.87 1.36
## [616,] 28.0 71.0 0.99970 3.50 0.57
## [617,] 28.0 71.0 0.99970 3.50 0.57
## [618,] 14.0 28.0 0.99820 3.03 0.93
## [619,] 4.0 17.0 1.00060 3.13 0.70
## [620,] 6.0 16.0 0.99880 3.09 0.80
## [621,] 16.0 112.0 0.99760 3.27 0.61
## [622,] 14.0 104.0 0.99760 3.28 0.62
## [623,] 9.0 32.0 0.99740 3.13 0.55
## [624,] 21.0 45.0 0.99740 3.49 0.96
## [625,] 21.0 58.0 0.99970 3.46 0.72
## [626,] 21.0 58.0 0.99970 3.46 0.72
## [627,] 5.0 15.0 0.99880 3.36 0.49
## [628,] 5.0 15.0 0.99880 3.36 0.49
## [629,] 7.0 31.0 0.99760 3.27 0.60
## [630,] 20.0 84.0 0.99640 3.21 0.61
## [631,] 7.0 31.0 0.99760 3.27 0.60
## [632,] 5.0 19.0 0.99880 3.25 0.63
## [633,] 21.0 43.0 0.99640 3.32 0.57
## [634,] 11.0 86.0 1.00100 3.43 0.64
## [635,] 46.0 102.0 0.99640 3.27 0.58
## [636,] 24.0 33.0 0.99540 3.27 0.55
## [637,] 30.0 147.0 0.99790 3.24 0.53
## [638,] 31.0 145.0 0.99780 3.24 0.53
## [639,] 7.0 23.0 0.99640 3.35 0.65
## [640,] 25.0 57.0 0.99700 3.18 1.36
## [641,] 16.0 40.0 0.99910 3.39 0.62
## [642,] 21.0 68.0 0.99920 3.46 0.63
## [643,] 16.0 40.0 0.99910 3.39 0.62
## [644,] 21.0 68.0 0.99920 3.46 0.63
## [645,] 16.0 40.0 0.99910 3.39 0.62
## [646,] 5.0 11.0 0.99840 3.37 0.69
## [647,] 6.0 20.0 0.99720 3.40 0.63
## [648,] 5.0 14.0 0.99670 3.32 0.58
## [649,] 10.0 28.0 0.99640 3.33 0.67
## [650,] 24.0 148.0 0.99480 3.16 0.57
## [651,] 8.0 32.0 0.99860 2.89 0.50
## [652,] 35.0 155.0 1.00100 3.41 0.67
## [653,] 22.0 71.0 0.99760 2.98 0.84
## [654,] 9.0 30.0 0.99760 3.12 0.54
## [655,] 7.0 29.0 0.99790 3.08 0.46
## [656,] 20.0 53.0 1.00040 3.14 0.61
## [657,] 8.0 32.0 0.99860 2.89 0.50
## [658,] 23.0 42.0 0.99800 2.92 0.68
## [659,] 5.0 20.0 0.99730 3.32 0.81
## [660,] 5.0 13.0 0.99700 3.41 0.57
## [661,] 5.0 20.0 0.99730 3.32 0.81
## [662,] 15.0 42.0 0.99780 3.31 0.64
## [663,] 9.0 27.0 0.99720 3.36 0.70
## [664,] 5.0 13.0 0.99740 3.04 0.79
## [665,] 15.0 54.0 1.00000 3.03 0.66
## [666,] 25.0 48.0 0.99810 3.14 0.56
## [667,] 6.0 16.0 0.99800 3.18 0.60
## [668,] 6.0 15.0 0.99880 2.94 0.66
## [669,] 15.0 31.0 0.99660 3.15 0.57
## [670,] 6.0 15.0 0.99880 2.94 0.66
## [671,] 30.0 45.0 0.99590 3.26 0.58
## [672,] 5.0 13.0 0.99680 3.20 0.52
## [673,] 32.0 151.0 0.99800 3.15 0.53
## [674,] 5.0 13.0 0.99680 3.20 0.52
## [675,] 7.0 17.0 0.99840 3.08 0.67
## [676,] 12.0 31.0 0.99840 3.26 0.65
## [677,] 7.0 17.0 0.99840 3.08 0.67
## [678,] 12.0 31.0 0.99790 3.40 0.48
## [679,] 45.0 87.0 0.99830 3.48 0.53
## [680,] 20.0 49.0 0.99720 3.13 0.54
## [681,] 15.0 40.0 1.00040 3.06 0.49
## [682,] 16.0 29.0 0.99620 3.21 0.49
## [683,] 32.0 58.0 0.99800 3.33 0.54
## [684,] 5.0 15.0 0.99700 3.37 0.56
## [685,] 35.0 152.0 0.99800 3.25 0.48
## [686,] 5.0 15.0 0.99700 3.37 0.56
## [687,] 13.0 40.0 0.99700 3.44 0.60
## [688,] 13.0 38.0 0.99980 3.28 0.59
## [689,] 4.0 9.0 0.99620 3.40 0.47
## [690,] 5.0 17.0 0.99760 3.30 1.05
## [691,] 5.0 14.0 0.99660 3.63 0.54
## [692,] 12.0 93.0 0.99980 3.48 0.54
## [693,] 16.0 62.0 0.99790 3.03 1.17
## [694,] 21.0 122.0 0.99840 3.32 0.62
## [695,] 24.0 125.0 0.99840 3.31 0.61
## [696,] 18.0 44.0 0.99210 3.90 0.62
## [697,] 8.0 25.0 0.99720 3.47 0.67
## [698,] 8.0 25.0 0.99720 3.47 0.67
## [699,] 18.0 72.0 1.00010 3.31 0.53
## [700,] 5.0 19.0 0.99860 3.11 0.62
## [701,] 26.0 88.0 0.99840 3.08 0.57
## [702,] 8.0 25.0 0.99720 3.47 0.67
## [703,] 9.0 23.0 0.99700 3.47 0.67
## [704,] 22.0 84.0 0.99720 3.32 0.70
## [705,] 4.0 14.0 0.99800 3.29 0.54
## [706,] 11.0 54.0 0.99900 3.37 0.49
## [707,] 10.0 19.0 0.99560 3.40 0.47
## [708,] 16.0 37.0 0.99660 3.37 0.51
## [709,] 11.0 35.0 0.99600 3.34 0.61
## [710,] 13.0 33.0 0.99830 3.27 0.66
## [711,] 21.0 84.0 0.99850 3.06 0.57
## [712,] 16.0 112.0 0.99980 3.38 0.61
## [713,] 13.0 33.0 0.99920 3.36 0.45
## [714,] 10.0 48.0 0.99760 3.34 0.46
## [715,] 21.0 77.0 0.99800 3.28 0.51
## [716,] 13.0 34.0 0.99670 3.29 0.48
## [717,] 10.0 48.0 0.99760 3.34 0.46
## [718,] 12.0 49.0 0.99680 3.21 0.57
## [719,] 10.0 22.0 0.99760 3.22 0.44
## [720,] 17.0 45.0 0.99760 3.46 0.54
## [721,] 10.0 22.0 0.99760 3.22 0.44
## [722,] 35.0 106.0 0.99820 3.10 0.53
## [723,] 15.0 48.0 0.99680 3.21 0.59
## [724,] 36.0 127.0 0.99650 2.94 1.62
## [725,] 5.0 12.0 0.99580 3.54 0.60
## [726,] 5.0 13.0 0.99760 3.29 0.55
## [727,] 18.0 49.0 0.99940 3.43 0.72
## [728,] 4.0 11.0 0.99700 3.46 0.68
## [729,] 4.0 11.0 0.99700 3.46 0.68
## [730,] 27.0 58.0 0.99500 3.61 0.49
## [731,] 12.0 37.0 0.99820 3.17 0.67
## [732,] 4.0 14.0 0.99940 3.44 0.52
## [733,] 10.0 19.0 0.99660 3.39 0.47
## [734,] 16.0 72.0 0.99800 3.55 0.60
## [735,] 9.0 39.0 0.99870 3.48 0.62
## [736,] 11.0 22.0 0.99630 3.26 0.50
## [737,] 11.0 22.0 0.99630 3.26 0.50
## [738,] 19.0 58.0 1.00020 3.50 0.65
## [739,] 28.0 104.0 0.99830 3.10 0.56
## [740,] 19.0 38.0 0.99900 3.35 0.60
## [741,] 12.0 16.0 0.99650 3.45 0.68
## [742,] 28.0 139.0 0.99788 3.21 0.57
## [743,] 9.0 18.0 0.99720 3.46 0.65
## [744,] 25.0 101.0 1.00024 3.13 0.53
## [745,] 21.0 101.0 1.00010 3.13 0.51
## [746,] 12.0 28.0 0.99768 3.52 0.73
## [747,] 12.0 57.0 0.99780 3.30 0.47
## [748,] 16.0 68.0 0.99782 3.30 0.48
## [749,] 12.0 31.0 0.99761 3.52 0.72
## [750,] 12.0 28.0 0.99768 3.52 0.73
## [751,] 17.0 40.0 0.99803 3.29 0.55
## [752,] 17.0 40.0 0.99803 3.29 0.55
## [753,] 24.0 66.0 0.99785 3.39 0.61
## [754,] 17.0 40.0 0.99803 3.29 0.55
## [755,] 14.0 32.0 0.99656 3.09 1.06
## [756,] 22.0 47.0 0.99525 3.51 0.43
## [757,] 15.0 33.0 0.99488 3.60 0.46
## [758,] 10.0 31.0 0.99656 3.25 0.50
## [759,] 10.0 31.0 0.99656 3.25 0.50
## [760,] 33.0 88.0 0.99823 3.19 0.52
## [761,] 9.0 104.0 0.99779 3.23 0.57
## [762,] 5.0 35.0 0.99738 3.25 0.42
## [763,] 8.0 19.0 0.99701 3.31 0.53
## [764,] 5.0 35.0 0.99738 3.25 0.42
## [765,] 11.0 44.0 0.99888 3.31 0.55
## [766,] 12.0 48.0 0.99888 3.31 0.54
## [767,] 12.0 74.0 0.99738 3.14 0.54
## [768,] 13.0 98.0 0.99938 3.45 0.62
## [769,] 24.0 94.0 0.99744 3.55 0.53
## [770,] 7.0 32.0 0.99668 3.39 0.54
## [771,] 24.0 94.0 0.99744 3.55 0.53
## [772,] 23.0 143.0 0.99780 3.28 0.55
## [773,] 23.0 144.0 0.99782 3.27 0.55
## [774,] 1.0 44.0 0.99730 3.30 0.92
## [775,] 2.0 45.0 0.99727 3.31 0.91
## [776,] 7.0 16.0 0.99586 3.43 0.46
## [777,] 5.5 48.0 0.99612 3.40 0.60
## [778,] 6.0 18.0 0.99610 3.40 0.59
## [779,] 7.0 34.0 0.99788 3.36 0.61
## [780,] 21.0 92.0 0.99745 3.50 0.60
## [781,] 12.0 45.0 0.99676 3.31 0.60
## [782,] 9.0 37.0 0.99732 3.66 0.65
## [783,] 26.0 96.0 0.99814 3.36 0.53
## [784,] 9.0 37.0 0.99732 3.66 0.65
## [785,] 20.0 85.0 0.99746 3.55 0.59
## [786,] 11.0 61.0 0.99820 3.21 0.50
## [787,] 11.0 61.0 0.99820 3.21 0.50
## [788,] 19.0 58.0 0.99910 3.18 0.56
## [789,] 19.0 58.0 0.99910 3.18 0.56
## [790,] 21.0 119.0 0.99800 3.09 0.52
## [791,] 18.0 82.0 0.99708 3.33 0.68
## [792,] 25.0 130.0 0.99818 3.23 0.54
## [793,] 17.0 87.0 0.99745 3.48 0.60
## [794,] 7.0 13.0 0.99639 3.38 0.56
## [795,] 7.0 26.0 0.99531 3.17 0.53
## [796,] 7.0 60.0 0.99786 2.99 1.18
## [797,] 24.0 64.0 0.99746 3.10 0.74
## [798,] 21.0 42.0 0.99526 3.24 0.81
## [799,] 5.0 14.0 0.99870 3.29 0.52
## [800,] 5.0 14.0 0.99870 3.29 0.52
## [801,] 26.0 108.0 0.99641 3.25 0.51
## [802,] 8.0 17.0 0.99735 3.23 0.44
## [803,] 14.0 86.0 0.99264 3.56 0.94
## [804,] 14.0 46.0 0.99710 3.24 0.66
## [805,] 4.0 18.0 0.99682 3.26 0.57
## [806,] 4.0 10.0 0.99356 3.33 0.70
## [807,] 4.0 10.0 0.99386 3.27 0.71
## [808,] 4.0 10.0 0.99356 3.33 0.70
## [809,] 4.0 12.0 0.99702 3.26 0.86
## [810,] 4.0 15.0 0.99693 3.22 0.55
## [811,] 4.0 14.0 0.99562 3.30 0.47
## [812,] 7.0 24.0 1.00012 3.09 0.68
## [813,] 20.0 38.0 0.99818 3.24 0.71
## [814,] 4.0 7.0 0.99462 3.44 0.58
## [815,] 19.0 41.0 0.99939 3.21 0.76
## [816,] 20.0 38.0 0.99818 3.24 0.71
## [817,] 8.0 30.0 0.99840 3.23 0.58
## [818,] 19.0 27.0 0.99545 3.28 0.73
## [819,] 21.0 47.0 0.99632 3.29 0.45
## [820,] 9.0 59.0 0.99976 3.28 0.54
## [821,] 9.0 22.0 0.99606 3.34 0.60
## [822,] 16.0 42.0 0.99154 3.71 0.74
## [823,] 15.0 36.0 0.99730 3.61 0.64
## [824,] 15.0 36.0 0.99730 3.61 0.64
## [825,] 6.0 16.0 0.99682 3.24 0.53
## [826,] 15.0 37.0 0.99624 3.36 0.49
## [827,] 4.0 8.0 0.99510 3.40 0.64
## [828,] 15.0 37.0 0.99624 3.36 0.49
## [829,] 34.0 45.0 0.99417 3.46 0.74
## [830,] 16.0 24.0 0.99376 3.56 0.77
## [831,] 5.0 9.0 0.99632 3.38 0.55
## [832,] 16.0 24.0 0.99376 3.56 0.77
## [833,] 34.0 48.0 0.99832 3.38 0.86
## [834,] 36.0 51.0 0.99836 3.38 0.86
## [835,] 16.0 23.0 0.99694 3.32 0.47
## [836,] 27.0 55.0 0.99655 3.39 0.51
## [837,] 36.0 100.0 0.99064 3.26 0.39
## [838,] 36.0 100.0 0.99064 3.26 0.39
## [839,] 9.0 28.0 0.99672 3.24 0.83
## [840,] 13.0 26.0 0.99647 3.46 0.47
## [841,] 9.0 34.0 0.99736 3.24 0.77
## [842,] 21.0 31.0 0.99629 3.45 0.63
## [843,] 34.0 68.0 0.99708 3.23 0.72
## [844,] 9.0 92.0 0.99630 3.28 0.62
## [845,] 26.0 42.0 0.99590 3.18 0.83
## [846,] 14.0 31.0 0.99689 3.59 0.66
## [847,] 14.0 31.0 0.99689 3.59 0.66
## [848,] 12.0 39.0 0.99770 3.50 0.70
## [849,] 14.0 31.0 0.99689 3.59 0.66
## [850,] 12.0 32.0 0.99689 3.58 0.66
## [851,] 9.0 22.0 0.99708 3.28 0.55
## [852,] 9.0 22.0 0.99708 3.28 0.55
## [853,] 26.0 122.0 0.99801 3.22 1.07
## [854,] 41.0 55.0 0.99652 3.47 0.73
## [855,] 41.0 55.0 0.99652 3.47 0.73
## [856,] 10.0 14.0 0.99538 3.51 0.71
## [857,] 41.0 55.0 0.99652 3.47 0.73
## [858,] 16.0 47.0 0.99594 3.40 0.78
## [859,] 17.0 32.0 0.99686 3.15 0.67
## [860,] 15.0 24.0 0.99438 3.40 0.82
## [861,] 15.0 85.0 0.99746 3.51 0.54
## [862,] 15.0 88.0 0.99357 3.66 0.60
## [863,] 7.0 25.0 0.99628 3.24 0.44
## [864,] 17.0 84.0 0.99746 3.51 0.53
## [865,] 15.0 85.0 0.99746 3.51 0.54
## [866,] 16.0 86.0 0.99748 3.51 0.54
## [867,] 13.0 24.0 0.99438 3.41 0.83
## [868,] 13.0 22.0 0.99438 3.40 0.84
## [869,] 15.0 24.0 0.99438 3.40 0.82
## [870,] 27.0 43.0 0.99578 3.44 0.64
## [871,] 31.0 43.0 0.99371 3.41 0.57
## [872,] 36.0 46.0 0.99522 3.53 0.57
## [873,] 28.0 48.0 0.99576 3.43 0.54
## [874,] 6.0 10.0 0.99552 3.23 0.58
## [875,] 6.0 10.0 0.99664 3.12 0.65
## [876,] 7.0 16.0 0.99614 3.31 0.79
## [877,] 7.0 14.0 0.99517 3.40 0.58
## [878,] 31.0 43.0 0.99371 3.41 0.57
## [879,] 30.0 69.0 0.99787 3.22 0.50
## [880,] 18.0 88.0 0.99745 3.53 0.55
## [881,] 10.0 21.0 0.99576 3.15 0.49
## [882,] 30.0 35.0 0.99533 3.48 0.65
## [883,] 14.0 26.0 0.99536 3.22 0.78
## [884,] 18.0 88.0 0.99745 3.53 0.55
## [885,] 30.0 69.0 0.99787 3.22 0.50
## [886,] 9.0 30.0 0.99824 3.34 0.64
## [887,] 8.0 28.0 0.99836 3.33 0.65
## [888,] 29.0 56.0 0.99577 3.15 0.79
## [889,] 32.0 64.0 0.99491 3.43 0.56
## [890,] 23.0 99.0 1.00289 3.22 0.68
## [891,] 30.0 52.0 0.99576 3.44 0.58
## [892,] 16.0 86.0 0.99743 3.53 0.57
## [893,] 10.0 18.0 0.99774 3.22 0.65
## [894,] 16.0 86.0 0.99743 3.53 0.57
## [895,] 17.0 88.0 0.99745 3.53 0.58
## [896,] 27.0 43.0 0.99550 3.42 0.58
## [897,] 17.0 43.0 0.99444 3.31 0.77
## [898,] 27.0 43.0 0.99550 3.42 0.58
## [899,] 17.0 43.0 0.99444 3.31 0.77
## [900,] 6.0 11.0 0.99892 3.48 0.49
## [901,] 10.0 39.0 0.99562 3.40 0.69
## [902,] 16.0 33.0 0.99736 3.58 0.69
## [903,] 16.0 33.0 0.99736 3.58 0.69
## [904,] 11.0 18.0 0.99620 3.41 0.59
## [905,] 11.0 18.0 0.99620 3.41 0.59
## [906,] 15.0 115.0 0.99800 3.23 0.59
## [907,] 12.0 78.0 0.99640 3.39 0.71
## [908,] 6.0 9.0 0.99480 3.59 0.54
## [909,] 34.0 61.0 0.99528 3.43 0.59
## [910,] 7.0 11.0 0.99331 3.29 0.52
## [911,] 3.0 11.0 0.99577 3.23 0.57
## [912,] 3.0 9.0 0.99901 3.18 0.60
## [913,] 4.0 8.0 0.99674 3.33 0.62
## [914,] 3.0 10.0 0.99639 3.27 0.64
## [915,] 7.0 11.0 0.99331 3.29 0.52
## [916,] 3.0 6.0 0.99512 3.27 0.67
## [917,] 7.0 62.0 0.99395 3.62 0.61
## [918,] 26.0 45.0 0.99824 3.38 0.64
## [919,] 32.0 79.0 0.99640 3.30 0.72
## [920,] 38.0 46.0 0.99504 3.38 0.89
## [921,] 10.0 23.0 0.99786 3.24 0.56
## [922,] 32.0 79.0 0.99640 3.30 0.72
## [923,] 38.0 46.0 0.99504 3.38 0.89
## [924,] 26.0 45.0 0.99824 3.38 0.64
## [925,] 14.0 28.0 0.99516 3.18 0.80
## [926,] 53.0 77.0 0.99604 3.47 0.87
## [927,] 52.0 73.0 0.99786 3.47 0.90
## [928,] 11.0 75.0 0.99736 3.20 0.59
## [929,] 14.0 28.0 0.99516 3.18 0.80
## [930,] 10.0 19.0 0.99468 3.30 0.73
## [931,] 8.0 25.0 0.99746 3.69 0.73
## [932,] 13.0 38.0 0.99748 3.48 0.65
## [933,] 29.0 66.0 0.99710 3.45 0.59
## [934,] 13.0 38.0 0.99748 3.48 0.65
## [935,] 8.0 25.0 0.99746 3.69 0.73
## [936,] 19.0 72.0 0.99543 3.39 0.72
## [937,] 19.0 72.0 0.99543 3.39 0.72
## [938,] 6.0 26.0 0.99791 3.07 0.60
## [939,] 23.0 42.0 0.99356 3.34 0.72
## [940,] 7.0 11.0 0.99425 3.61 0.54
## [941,] 13.0 25.0 0.99509 3.36 0.76
## [942,] 9.0 17.0 0.99484 3.19 0.52
## [943,] 13.0 52.0 0.99834 3.22 0.64
## [944,] 10.0 45.0 0.99864 3.24 0.60
## [945,] 11.0 24.0 0.99498 3.27 0.64
## [946,] 7.0 20.0 0.99566 3.14 0.79
## [947,] 11.0 24.0 0.99745 3.29 0.99
## [948,] 6.0 12.0 0.99408 3.26 0.62
## [949,] 10.0 21.0 0.99552 3.41 0.76
## [950,] 10.0 21.0 0.99552 3.41 0.76
## [951,] 10.0 21.0 0.99552 3.41 0.76
## [952,] 6.0 12.0 0.99408 3.26 0.62
## [953,] 6.0 10.0 0.99536 3.31 0.68
## [954,] 5.0 9.0 0.99458 3.20 0.69
## [955,] 6.0 11.0 0.99538 3.44 0.54
## [956,] 9.0 23.0 0.99648 3.36 0.67
## [957,] 5.0 10.0 0.99568 3.31 0.62
## [958,] 12.0 51.0 0.99613 3.29 0.58
## [959,] 25.0 36.0 0.99519 3.47 0.71
## [960,] 12.0 32.0 0.99735 3.36 0.61
## [961,] 18.0 38.0 0.99518 3.16 0.85
## [962,] 7.0 18.0 0.99592 3.27 0.62
## [963,] 6.0 16.0 0.99654 3.38 0.69
## [964,] 20.0 27.0 0.99546 3.15 0.69
## [965,] 18.0 38.0 0.99518 3.16 0.85
## [966,] 24.0 48.0 0.99554 3.34 0.86
## [967,] 6.0 10.0 0.99604 3.13 0.58
## [968,] 23.0 113.0 0.99733 3.13 0.48
## [969,] 29.0 46.0 0.99430 3.20 0.60
## [970,] 7.0 22.0 0.99669 3.46 0.61
## [971,] 6.0 10.0 0.99724 3.33 0.87
## [972,] 6.0 10.0 0.99724 3.33 0.87
## [973,] 6.0 13.0 0.99643 3.22 0.65
## [974,] 6.0 12.0 0.99605 3.52 0.63
## [975,] 7.0 13.0 0.99658 3.30 0.62
## [976,] 35.0 72.0 0.99700 3.44 0.52
## [977,] 35.0 72.0 0.99700 3.44 0.52
## [978,] 31.0 119.0 0.99801 3.15 0.50
## [979,] 9.0 29.0 0.99416 3.28 0.49
## [980,] 3.0 6.0 0.99690 3.13 0.63
## [981,] 8.0 17.0 0.99774 3.32 0.71
## [982,] 13.0 28.0 0.99712 3.25 0.62
## [983,] 51.0 70.0 0.99418 3.34 0.82
## [984,] 8.0 17.0 0.99774 3.32 0.71
## [985,] 3.0 6.0 0.99690 3.13 0.63
## [986,] 7.0 11.0 0.99562 3.45 0.58
## [987,] 3.0 7.0 0.99470 3.19 0.55
## [988,] 35.0 70.0 0.99693 3.44 0.50
## [989,] 19.0 27.0 0.99596 3.16 0.49
## [990,] 14.0 21.0 0.99556 3.14 0.51
## [991,] 19.0 27.0 0.99596 3.16 0.49
## [992,] 31.0 68.0 0.99694 3.45 0.48
## [993,] 30.0 47.0 0.99554 3.36 0.48
## [994,] 31.0 68.0 0.99694 3.45 0.48
## [995,] 20.0 88.0 0.99918 3.15 0.43
## [996,] 19.0 41.0 0.99697 3.39 0.62
## [997,] 3.0 11.0 0.99378 3.71 0.63
## [998,] 3.0 11.0 0.99378 3.71 0.63
## [999,] 4.0 10.0 0.99554 3.12 0.48
## [1000,] 7.0 12.0 0.99162 3.47 0.53
## [1001,] 6.0 12.0 0.99495 3.44 0.72
## [1002,] 31.0 47.0 0.99676 3.26 0.82
## [1003,] 13.0 27.0 0.99516 3.26 0.84
## [1004,] 4.0 8.0 0.99280 3.36 0.55
## [1005,] 27.0 45.0 0.99603 3.25 0.54
## [1006,] 4.0 8.0 0.99280 3.36 0.55
## [1007,] 13.0 27.0 0.99516 3.26 0.84
## [1008,] 12.0 25.0 0.99516 3.26 0.84
## [1009,] 12.0 24.0 0.99549 3.23 0.70
## [1010,] 26.0 55.0 0.99722 3.18 0.68
## [1011,] 7.0 12.0 0.99354 3.25 0.55
## [1012,] 12.0 19.0 0.99570 3.17 0.55
## [1013,] 11.0 32.0 0.99604 3.23 0.48
## [1014,] 22.0 31.0 0.99635 3.38 0.58
## [1015,] 3.0 7.0 0.99454 3.22 0.58
## [1016,] 14.0 27.0 0.99598 3.06 0.84
## [1017,] 12.0 28.0 0.99486 3.27 0.75
## [1018,] 36.0 109.0 0.99007 2.89 0.44
## [1019,] 36.0 109.0 0.99007 2.89 0.44
## [1020,] 10.0 23.0 0.99636 3.53 0.61
## [1021,] 3.0 8.0 0.99642 3.20 0.53
## [1022,] 3.0 8.0 0.99642 3.20 0.53
## [1023,] 4.0 9.0 0.99584 3.35 0.54
## [1024,] 3.0 9.0 0.99506 3.27 0.55
## [1025,] 12.0 18.0 0.99568 3.32 0.56
## [1026,] 17.0 43.0 0.99822 3.33 0.60
## [1027,] 14.0 36.0 0.99364 3.41 0.56
## [1028,] 28.0 52.0 0.99378 3.49 0.52
## [1029,] 41.0 68.0 0.99586 3.37 0.54
## [1030,] 12.0 18.0 0.99568 3.32 0.56
## [1031,] 9.0 14.0 0.99488 3.42 0.55
## [1032,] 9.0 15.0 0.99514 3.35 0.58
## [1033,] 5.0 14.0 0.99854 3.36 0.53
## [1034,] 14.0 27.0 0.99592 3.30 0.59
## [1035,] 15.0 48.0 0.99739 3.20 0.47
## [1036,] 5.0 17.0 0.99683 3.17 0.65
## [1037,] 26.0 35.0 0.99356 3.22 0.59
## [1038,] 20.0 56.0 0.99672 3.35 0.56
## [1039,] 25.0 42.0 0.99530 3.24 0.77
## [1040,] 21.0 39.0 0.99692 3.33 0.83
## [1041,] 16.0 32.0 0.99756 3.58 0.67
## [1042,] 13.0 26.0 0.99547 3.38 0.64
## [1043,] 21.0 39.0 0.99692 3.33 0.83
## [1044,] 18.0 39.0 0.99859 3.29 0.81
## [1045,] 12.0 53.0 0.99294 3.36 0.62
## [1046,] 32.0 38.0 0.99438 3.32 0.58
## [1047,] 33.0 45.0 0.99612 3.31 0.62
## [1048,] 27.0 51.0 0.99634 3.49 0.64
## [1049,] 3.0 14.0 0.99702 3.27 0.78
## [1050,] 6.0 17.0 0.99704 3.27 0.77
## [1051,] 27.0 51.0 0.99634 3.49 0.64
## [1052,] 16.0 45.0 0.99702 3.03 1.34
## [1053,] 19.0 25.0 0.99258 3.56 0.55
## [1054,] 9.0 20.0 0.99426 3.38 0.77
## [1055,] 5.0 77.0 0.99747 3.13 0.62
## [1056,] 5.0 77.0 0.99747 3.13 0.62
## [1057,] 3.0 10.0 0.99586 3.21 0.59
## [1058,] 28.0 90.0 0.99784 3.15 0.57
## [1059,] 30.0 52.0 0.99710 3.19 0.76
## [1060,] 3.0 10.0 0.99586 3.21 0.59
## [1061,] 3.0 8.0 0.99810 3.14 0.70
## [1062,] 7.0 16.0 0.99462 3.21 0.69
## [1063,] 6.0 15.0 0.99560 3.30 0.64
## [1064,] 6.0 17.0 0.99565 3.22 0.63
## [1065,] 5.0 10.0 0.99418 3.28 0.57
## [1066,] 6.0 20.0 0.99630 3.29 0.60
## [1067,] 13.0 26.0 0.99358 3.40 0.72
## [1068,] 3.0 10.0 0.99572 3.02 0.83
## [1069,] 3.0 10.0 0.99572 3.02 0.83
## [1070,] 28.0 52.0 0.99700 3.31 0.62
## [1071,] 19.0 37.0 0.99498 3.18 0.89
## [1072,] 30.0 92.0 0.99892 3.20 0.58
## [1073,] 33.0 75.0 0.99680 3.40 0.58
## [1074,] 16.0 37.0 0.99720 3.31 0.58
## [1075,] 30.0 92.0 0.99892 3.20 0.58
## [1076,] 45.0 67.0 0.99769 3.44 0.86
## [1077,] 3.0 8.0 0.99534 3.15 0.73
## [1078,] 3.0 8.0 0.99817 3.27 0.58
## [1079,] 3.0 8.0 0.99817 3.27 0.58
## [1080,] 37.5 278.0 0.99316 3.01 0.51
## [1081,] 3.0 8.0 0.99471 3.16 0.51
## [1082,] 37.5 289.0 0.99316 3.01 0.51
## [1083,] 31.0 70.0 0.99685 3.42 0.59
## [1084,] 32.0 59.0 0.99617 3.33 0.77
## [1085,] 31.0 70.0 0.99685 3.42 0.59
## [1086,] 40.0 64.0 0.99529 3.12 0.49
## [1087,] 3.0 9.0 0.99738 3.38 0.66
## [1088,] 18.0 30.0 0.99400 3.29 0.69
## [1089,] 22.0 41.0 0.99735 3.02 0.76
## [1090,] 22.0 41.0 0.99735 3.02 0.76
## [1091,] 42.0 74.0 0.99451 2.98 0.63
## [1092,] 8.0 19.0 0.99546 3.35 0.60
## [1093,] 10.0 16.0 0.99479 3.43 0.59
## [1094,] 11.0 31.0 0.99615 3.33 0.86
## [1095,] 9.0 17.0 0.99655 3.35 0.49
## [1096,] 6.0 20.0 0.99772 3.15 0.50
## [1097,] 9.0 17.0 0.99655 3.35 0.49
## [1098,] 5.0 18.0 0.99666 3.20 0.52
## [1099,] 5.0 16.0 0.99358 3.15 0.81
## [1100,] 5.0 18.0 0.99666 3.20 0.52
## [1101,] 23.0 36.0 0.99392 3.11 0.78
## [1102,] 14.0 25.0 0.99388 3.35 0.63
## [1103,] 18.0 30.0 0.99402 3.45 0.54
## [1104,] 14.0 25.0 0.99388 3.35 0.63
## [1105,] 16.0 25.0 0.99360 3.28 0.66
## [1106,] 13.0 49.0 0.99374 3.41 0.60
## [1107,] 9.0 17.0 0.99376 3.21 0.54
## [1108,] 10.0 24.0 0.99523 3.27 0.85
## [1109,] 4.0 13.0 0.99855 3.36 0.49
## [1110,] 27.0 66.0 0.99820 3.17 0.76
## [1111,] 6.0 14.0 0.99593 3.25 0.64
## [1112,] 23.0 55.0 0.99471 3.78 0.64
## [1113,] 6.0 35.0 0.99396 3.29 0.71
## [1114,] 3.0 10.0 0.99698 3.12 0.59
## [1115,] 29.0 80.0 0.99020 3.49 0.66
## [1116,] 15.0 21.0 0.99572 3.38 0.60
## [1117,] 15.0 21.0 0.99572 3.38 0.60
## [1118,] 15.0 21.0 0.99572 3.38 0.60
## [1119,] 14.0 24.0 0.99252 3.30 0.53
## [1120,] 7.0 15.0 0.99256 3.52 0.58
## [1121,] 8.0 17.0 0.99235 3.20 0.72
## [1122,] 21.0 28.0 0.99352 3.44 0.55
## [1123,] 27.0 33.0 0.99220 3.45 0.48
## [1124,] 17.0 29.0 0.99674 3.12 0.65
## [1125,] 3.0 13.0 0.99557 3.62 0.62
## [1126,] 13.0 27.0 0.99394 3.14 0.59
## [1127,] 3.0 11.0 0.99150 3.39 0.54
## [1128,] 26.0 52.0 0.99379 3.51 0.60
## [1129,] 28.0 89.0 0.99840 3.22 0.68
## [1130,] 24.0 70.0 0.99798 3.21 0.69
## [1131,] 5.0 10.0 0.99770 3.18 0.63
## [1132,] 57.0 135.0 0.99341 3.32 0.44
## [1133,] 18.0 38.0 0.99330 3.38 0.88
## [1134,] 10.0 18.0 0.99684 3.37 0.68
## [1135,] 6.0 15.0 0.99524 3.30 0.74
## [1136,] 22.0 50.0 0.99460 3.38 0.60
## [1137,] 6.0 13.0 0.99774 3.22 0.76
## [1138,] 6.0 13.0 0.99774 3.22 0.76
## [1139,] 29.0 94.0 0.99786 3.14 0.58
## [1140,] 16.0 86.0 0.99764 3.34 0.64
## [1141,] 33.0 79.0 0.99690 3.41 0.65
## [1142,] 24.0 71.0 0.99624 3.27 0.85
## [1143,] 6.0 12.0 0.99354 3.30 0.65
## [1144,] 16.0 20.0 0.99672 3.61 0.82
## [1145,] 35.0 70.0 0.99588 3.43 0.62
## [1146,] 31.0 51.0 0.99672 3.53 0.81
## [1147,] 6.0 21.0 0.99600 3.28 0.87
## [1148,] 6.0 14.0 0.99702 3.21 0.49
## [1149,] 9.0 21.0 0.99526 3.39 0.66
## [1150,] 6.0 11.0 0.99585 3.23 0.52
## [1151,] 4.0 12.0 0.99473 3.30 0.76
## [1152,] 16.0 70.0 0.99352 3.46 0.65
## [1153,] 9.0 38.0 0.99616 3.15 0.53
## [1154,] 17.0 38.0 0.99622 3.23 0.66
## [1155,] 50.0 63.0 0.99544 3.59 0.68
## [1156,] 9.0 38.0 0.99616 3.15 0.53
## [1157,] 45.0 88.0 0.99524 3.33 0.76
## [1158,] 16.0 101.0 0.99240 3.46 0.87
## [1159,] 22.0 54.0 0.99572 3.42 1.16
## [1160,] 11.0 37.0 0.99728 3.16 0.67
## [1161,] 6.0 20.0 0.99676 3.14 0.72
## [1162,] 12.0 21.0 0.99551 3.21 0.75
## [1163,] 12.0 19.0 0.99434 3.14 0.71
## [1164,] 10.0 21.0 0.99692 3.29 0.67
## [1165,] 10.0 21.0 0.99692 3.29 0.67
## [1166,] 15.0 38.0 0.99634 3.01 1.10
## [1167,] 7.0 60.0 0.99709 2.94 0.98
## [1168,] 29.0 48.0 0.99528 3.32 0.88
## [1169,] 8.0 44.0 0.99384 3.21 0.56
## [1170,] 5.0 14.0 0.99502 3.32 0.62
## [1171,] 5.0 12.0 0.99667 3.20 0.67
## [1172,] 26.0 44.0 0.99522 3.42 0.68
## [1173,] 5.0 16.0 0.99649 3.27 0.74
## [1174,] 26.0 65.0 0.99716 3.46 0.62
## [1175,] 26.0 65.0 0.99716 3.46 0.62
## [1176,] 48.0 59.0 0.99541 3.61 0.70
## [1177,] 23.0 47.0 0.99572 3.58 0.50
## [1178,] 6.0 11.0 0.99318 3.35 0.66
## [1179,] 17.0 78.0 0.99346 3.68 0.73
## [1180,] 11.0 47.0 0.99599 3.27 0.81
## [1181,] 11.0 47.0 0.99599 3.27 0.81
## [1182,] 5.0 11.0 0.99478 3.19 0.46
## [1183,] 41.0 54.0 0.99754 3.38 0.86
## [1184,] 16.0 61.0 0.99572 3.29 0.60
## [1185,] 11.0 119.0 0.99538 3.36 0.70
## [1186,] 6.0 39.0 0.99346 3.33 0.46
## [1187,] 6.0 12.0 0.99630 3.52 0.50
## [1188,] 6.0 39.0 0.99346 3.33 0.46
## [1189,] 11.0 119.0 0.99538 3.36 0.70
## [1190,] 5.0 19.0 0.99616 3.30 0.44
## [1191,] 6.0 20.0 0.99652 3.28 0.57
## [1192,] 6.0 12.0 0.99551 3.56 0.51
## [1193,] 11.0 41.0 0.99439 3.52 0.80
## [1194,] 6.0 12.0 0.99551 3.56 0.51
## [1195,] 15.0 64.0 0.99588 3.22 0.59
## [1196,] 21.0 56.0 0.99633 3.52 0.60
## [1197,] 23.0 94.0 0.99686 3.21 0.58
## [1198,] 4.0 9.0 0.99458 3.16 0.54
## [1199,] 19.0 77.0 0.99510 3.15 0.79
## [1200,] 23.0 94.0 0.99686 3.21 0.58
## [1201,] 4.0 9.0 0.99458 3.16 0.54
## [1202,] 5.0 10.0 0.99419 3.27 0.54
## [1203,] 6.0 12.0 0.99516 3.35 0.69
## [1204,] 33.0 76.0 0.99878 3.14 0.55
## [1205,] 24.0 44.0 0.99534 3.40 0.85
## [1206,] 24.0 44.0 0.99534 3.40 0.85
## [1207,] 24.0 44.0 0.99534 3.40 0.85
## [1208,] 24.0 52.0 0.99752 3.35 0.94
## [1209,] 24.0 44.0 0.99534 3.40 0.85
## [1210,] 14.0 24.0 0.99428 3.45 0.87
## [1211,] 8.0 15.0 0.99498 3.35 0.62
## [1212,] 22.0 53.0 0.99570 3.32 0.62
## [1213,] 8.0 15.0 0.99498 3.35 0.62
## [1214,] 5.0 13.0 0.99659 3.15 0.75
## [1215,] 6.0 9.0 0.99628 3.10 0.48
## [1216,] 20.0 29.0 0.99488 3.26 0.56
## [1217,] 10.0 79.0 0.99677 3.29 0.69
## [1218,] 43.0 74.0 0.99408 3.23 0.81
## [1219,] 8.0 24.0 0.99600 3.24 0.69
## [1220,] 25.0 50.0 0.99478 3.20 0.83
## [1221,] 17.0 44.0 0.99734 3.28 0.77
## [1222,] 17.0 44.0 0.99734 3.28 0.77
## [1223,] 33.0 89.0 0.99678 3.26 0.46
## [1224,] 9.0 23.0 0.99638 3.23 0.76
## [1225,] 8.0 20.0 0.99920 3.07 0.82
## [1226,] 18.0 77.0 0.99922 3.15 0.51
## [1227,] 27.0 46.0 0.99592 3.02 0.47
## [1228,] 8.0 21.0 0.99769 3.27 0.72
## [1229,] 18.0 88.0 0.99157 3.68 0.73
## [1230,] 19.0 66.0 0.99718 3.40 0.64
## [1231,] 15.0 58.0 0.99470 3.37 0.78
## [1232,] 48.0 90.0 0.99621 3.38 0.62
## [1233,] 19.0 66.0 0.99718 3.40 0.64
## [1234,] 14.0 36.0 0.99614 3.23 0.49
## [1235,] 11.0 18.0 0.99242 3.39 0.40
## [1236,] 6.0 113.0 0.99572 3.30 0.56
## [1237,] 7.0 17.0 0.99659 3.26 0.64
## [1238,] 11.0 18.0 0.99242 3.39 0.40
## [1239,] 12.0 24.0 0.99798 3.38 0.46
## [1240,] 11.0 20.0 0.99488 3.45 0.56
## [1241,] 36.0 60.0 0.99494 3.10 0.40
## [1242,] 28.0 65.0 0.99729 3.16 0.59
## [1243,] 15.0 40.0 0.99414 3.22 0.60
## [1244,] 10.0 86.0 0.99830 3.37 0.62
## [1245,] 72.0 160.0 0.99721 3.33 0.54
## [1246,] 15.0 34.0 0.99655 3.49 0.68
## [1247,] 15.0 48.0 0.99502 3.12 0.48
## [1248,] 15.0 34.0 0.99655 3.49 0.68
## [1249,] 22.0 36.0 0.99490 3.41 0.75
## [1250,] 24.0 37.0 0.99514 3.40 0.61
## [1251,] 24.0 37.0 0.99514 3.40 0.61
## [1252,] 27.0 60.0 0.99630 3.28 0.59
## [1253,] 6.0 14.0 0.99627 3.45 0.58
## [1254,] 6.0 13.0 0.99569 3.43 0.58
## [1255,] 20.0 35.0 0.99628 3.40 0.69
## [1256,] 26.0 46.0 0.99358 3.47 0.45
## [1257,] 43.0 60.0 0.99499 3.10 0.42
## [1258,] 12.0 69.0 0.99633 3.32 0.70
## [1259,] 15.0 33.0 0.99538 3.44 0.63
## [1260,] 15.0 33.0 0.99538 3.44 0.63
## [1261,] 19.0 56.0 0.99632 3.02 1.15
## [1262,] 17.0 24.0 0.99437 3.59 0.55
## [1263,] 34.0 66.0 0.99726 3.15 0.58
## [1264,] 13.0 26.0 0.99780 3.37 0.46
## [1265,] 26.0 51.0 0.99456 3.38 0.72
## [1266,] 16.0 36.0 0.99564 3.38 0.60
## [1267,] 16.0 36.0 0.99564 3.38 0.60
## [1268,] 13.0 19.0 0.99600 3.10 0.87
## [1269,] 21.0 51.0 0.99668 3.47 0.55
## [1270,] 28.0 87.0 0.99080 3.50 0.82
## [1271,] 26.0 60.0 0.99084 3.70 0.75
## [1272,] 24.0 64.0 0.99350 3.38 0.57
## [1273,] 25.0 44.0 0.99385 3.50 0.53
## [1274,] 34.0 44.0 0.99494 3.10 0.43
## [1275,] 17.0 36.0 0.99440 3.24 0.53
## [1276,] 13.0 81.0 0.99688 3.24 0.54
## [1277,] 3.0 10.0 0.99566 3.28 0.56
## [1278,] 3.0 10.0 0.99636 3.35 0.60
## [1279,] 13.0 81.0 0.99688 3.24 0.54
## [1280,] 3.0 9.0 0.99480 3.14 0.57
## [1281,] 28.0 54.0 0.99560 3.37 0.64
## [1282,] 28.0 54.0 0.99560 3.37 0.64
## [1283,] 9.0 22.0 0.99619 3.33 0.68
## [1284,] 17.0 69.0 0.99734 3.26 0.63
## [1285,] 18.0 36.0 0.99476 3.39 0.56
## [1286,] 20.0 47.0 0.99734 3.15 0.57
## [1287,] 17.0 31.0 0.99328 3.35 0.54
## [1288,] 3.0 7.0 0.99286 3.22 0.37
## [1289,] 20.0 110.0 0.99914 3.30 1.17
## [1290,] 20.0 110.0 0.99914 3.30 1.17
## [1291,] 6.0 12.0 0.99521 3.36 0.59
## [1292,] 25.0 60.0 0.99638 3.29 0.75
## [1293,] 14.0 28.0 0.99362 3.62 0.67
## [1294,] 6.0 12.0 0.99672 3.34 0.49
## [1295,] 25.0 60.0 0.99638 3.29 0.75
## [1296,] 51.0 77.5 0.99558 3.20 0.45
## [1297,] 51.0 77.5 0.99558 3.20 0.45
## [1298,] 15.0 29.0 0.99323 3.35 0.61
## [1299,] 11.0 18.0 0.99191 3.45 0.56
## [1300,] 5.0 9.0 0.99476 3.50 0.40
## [1301,] 15.0 28.0 0.99444 3.78 0.61
## [1302,] 20.0 57.0 0.99598 3.60 0.74
## [1303,] 4.0 15.0 0.99576 3.30 0.80
## [1304,] 28.0 68.0 0.99501 3.36 0.66
## [1305,] 21.0 68.0 0.99550 3.12 0.44
## [1306,] 24.0 88.0 0.99612 3.30 0.53
## [1307,] 22.0 91.0 0.99790 3.29 0.62
## [1308,] 15.0 29.0 0.99524 3.41 0.52
## [1309,] 22.0 91.0 0.99790 3.29 0.62
## [1310,] 27.0 63.0 0.99600 3.28 0.61
## [1311,] 24.0 88.0 0.99612 3.30 0.53
## [1312,] 12.0 27.0 0.99290 3.33 0.59
## [1313,] 14.0 41.0 0.99532 3.34 0.47
## [1314,] 20.0 65.0 0.99558 3.40 0.54
## [1315,] 24.0 69.0 0.99556 3.40 0.53
## [1316,] 17.0 91.0 0.99616 3.26 0.58
## [1317,] 16.0 46.0 0.99258 4.01 0.59
## [1318,] 10.0 41.0 0.99638 3.18 0.69
## [1319,] 17.0 91.0 0.99616 3.26 0.58
## [1320,] 18.0 64.0 0.99652 2.90 1.33
## [1321,] 12.0 88.0 0.99796 3.26 0.66
## [1322,] 16.0 46.0 0.99258 4.01 0.59
## [1323,] 9.0 18.0 0.99392 3.18 0.55
## [1324,] 21.0 55.0 0.99495 3.18 0.82
## [1325,] 18.0 34.0 0.99480 3.39 0.60
## [1326,] 18.0 34.0 0.99480 3.39 0.60
## [1327,] 18.0 34.0 0.99480 3.39 0.60
## [1328,] 18.0 34.0 0.99480 3.39 0.60
## [1329,] 13.0 38.0 0.99550 3.34 0.52
## [1330,] 17.0 91.0 0.99616 3.29 0.56
## [1331,] 17.0 91.0 0.99616 3.29 0.56
## [1332,] 31.0 67.0 0.99668 3.26 0.46
## [1333,] 6.0 25.0 0.99581 3.09 0.43
## [1334,] 12.0 48.0 0.99760 3.18 0.51
## [1335,] 4.0 11.0 0.99608 3.39 0.52
## [1336,] 19.0 40.0 0.99387 3.38 0.66
## [1337,] 15.0 26.0 0.99448 3.36 0.45
## [1338,] 15.0 26.0 0.99448 3.36 0.45
## [1339,] 15.0 26.0 0.99448 3.36 0.45
## [1340,] 13.0 31.0 0.99538 3.36 0.54
## [1341,] 13.0 31.0 0.99538 3.36 0.54
## [1342,] 13.0 31.0 0.99538 3.36 0.54
## [1343,] 17.0 31.0 0.99589 3.37 0.51
## [1344,] 13.0 31.0 0.99538 3.36 0.54
## [1345,] 8.0 20.0 0.99852 3.09 0.53
## [1346,] 10.0 28.0 0.99613 3.25 0.53
## [1347,] 5.0 13.0 0.99472 3.52 0.56
## [1348,] 7.0 12.0 0.99587 3.34 0.39
## [1349,] 7.0 12.0 0.99587 3.34 0.39
## [1350,] 21.0 41.0 0.99518 3.41 0.52
## [1351,] 32.0 73.0 0.99654 3.34 0.57
## [1352,] 8.0 46.0 0.99332 3.32 0.51
## [1353,] 14.0 57.0 0.99690 3.37 0.46
## [1354,] 14.0 57.0 0.99690 3.37 0.46
## [1355,] 7.0 28.0 0.99568 3.35 0.52
## [1356,] 5.0 32.0 0.99464 3.36 0.44
## [1357,] 4.0 28.0 0.99464 3.36 0.44
## [1358,] 27.0 67.0 0.99648 3.60 0.59
## [1359,] 52.0 98.0 0.99736 3.28 0.50
## [1360,] 6.0 28.0 0.99704 3.07 0.65
## [1361,] 11.0 38.0 0.99699 3.24 0.56
## [1362,] 13.0 54.0 0.99724 3.36 0.54
## [1363,] 6.0 28.0 0.99704 3.07 0.65
## [1364,] 11.0 63.0 0.99652 3.29 0.48
## [1365,] 10.0 31.0 0.99500 3.46 0.53
## [1366,] 10.0 42.0 0.99710 3.18 0.62
## [1367,] 10.0 45.0 0.99576 3.24 0.50
## [1368,] 9.0 105.0 0.99725 3.25 1.18
## [1369,] 16.0 74.0 0.99656 3.27 0.50
## [1370,] 4.0 8.0 0.99396 3.33 0.37
## [1371,] 12.0 66.0 0.99623 3.00 1.17
## [1372,] 5.0 14.0 0.99476 3.21 1.03
## [1373,] 12.0 66.0 0.99623 3.00 1.17
## [1374,] 34.0 89.0 0.99664 3.24 0.45
## [1375,] 16.0 29.0 0.99471 3.32 0.51
## [1376,] 13.0 100.0 0.99586 3.26 0.52
## [1377,] 7.0 31.0 0.99460 3.11 0.46
## [1378,] 23.0 74.0 0.99530 3.71 0.62
## [1379,] 10.0 28.0 0.99609 3.29 0.51
## [1380,] 11.0 35.0 0.99557 3.36 0.62
## [1381,] 11.0 35.0 0.99557 3.36 0.62
## [1382,] 4.0 25.0 0.99534 3.38 0.45
## [1383,] 25.0 105.0 0.99613 3.30 0.49
## [1384,] 25.0 105.0 0.99613 3.30 0.49
## [1385,] 20.0 84.0 0.99593 3.19 0.50
## [1386,] 34.0 85.0 0.99668 3.19 0.42
## [1387,] 8.0 23.0 0.99610 3.30 0.58
## [1388,] 8.0 23.0 0.99610 3.30 0.58
## [1389,] 7.0 49.0 0.99718 3.35 0.68
## [1390,] 36.0 111.0 0.99524 3.10 0.53
## [1391,] 15.0 33.0 0.99292 3.58 0.59
## [1392,] 5.0 33.0 0.99612 3.37 0.58
## [1393,] 5.0 12.0 0.99420 3.17 0.48
## [1394,] 19.0 59.0 0.99612 3.30 0.48
## [1395,] 27.0 73.0 0.99669 3.32 0.48
## [1396,] 3.0 12.0 0.99745 3.31 0.65
## [1397,] 4.0 11.0 0.99745 3.31 0.65
## [1398,] 17.0 104.0 0.99584 3.28 0.52
## [1399,] 13.0 28.0 0.99660 3.52 0.62
## [1400,] 10.0 24.0 0.99560 3.42 0.72
## [1401,] 33.0 141.0 0.99620 3.25 0.51
## [1402,] 33.0 141.0 0.99620 3.25 0.51
## [1403,] 6.0 24.0 0.99630 3.44 0.82
## [1404,] 3.0 13.0 0.99600 3.23 1.10
## [1405,] 12.0 46.0 0.99850 3.43 0.62
## [1406,] 18.0 34.0 0.99520 3.28 0.90
## [1407,] 8.0 22.0 0.99740 3.22 0.94
## [1408,] 40.0 54.0 0.99500 3.54 0.93
## [1409,] 35.0 53.0 0.99500 3.27 1.01
## [1410,] 40.0 54.0 0.99500 3.54 0.93
## [1411,] 5.0 16.0 0.99360 3.50 0.44
## [1412,] 8.0 19.0 0.99630 3.56 0.73
## [1413,] 8.0 22.0 0.99740 3.22 0.94
## [1414,] 12.0 89.0 0.99630 3.04 0.90
## [1415,] 3.0 15.0 0.99940 3.20 0.78
## [1416,] 8.0 18.0 0.99660 3.56 0.84
## [1417,] 3.0 15.0 0.99940 3.20 0.78
## [1418,] 21.0 37.0 0.99520 3.35 0.77
## [1419,] 3.0 19.0 0.99500 3.16 0.46
## [1420,] 32.0 133.0 0.99560 3.27 0.45
## [1421,] 3.0 19.0 0.99500 3.16 0.46
## [1422,] 24.0 58.0 0.99650 3.34 0.58
## [1423,] 39.0 55.0 0.99560 3.39 0.84
## [1424,] 14.0 31.0 0.99680 3.50 0.67
## [1425,] 8.0 23.0 0.99740 3.26 0.70
## [1426,] 8.0 23.0 0.99740 3.26 0.70
## [1427,] 23.0 60.0 0.99710 3.41 0.71
## [1428,] 6.0 23.0 0.99570 3.30 0.58
## [1429,] 27.0 55.0 0.99620 3.31 0.63
## [1430,] 38.0 67.0 0.99600 3.33 0.93
## [1431,] 18.0 47.0 0.99560 3.37 0.62
## [1432,] 13.0 74.0 0.99580 3.26 0.54
## [1433,] 3.0 11.0 0.99440 3.48 0.85
## [1434,] 11.0 25.0 0.99550 3.42 0.74
## [1435,] 55.0 95.0 1.00369 3.18 0.77
## [1436,] 55.0 95.0 1.00369 3.18 0.77
## [1437,] 27.0 90.0 0.99914 3.15 0.65
## [1438,] 15.0 39.0 0.99577 3.53 0.54
## [1439,] 3.0 8.0 0.99600 3.41 0.47
## [1440,] 31.0 92.0 0.99566 3.32 0.68
## [1441,] 15.0 28.0 0.99470 3.23 0.73
## [1442,] 19.0 98.0 0.99713 3.16 0.52
## [1443,] 18.0 30.0 0.99712 3.40 0.75
## [1444,] 8.0 22.0 0.99322 3.38 0.49
## [1445,] 31.0 92.0 0.99566 3.32 0.68
## [1446,] 19.0 98.0 0.99713 3.16 0.52
## [1447,] 18.0 30.0 0.99712 3.40 0.75
## [1448,] 22.0 39.0 0.99701 3.40 0.74
## [1449,] 40.0 54.0 0.99683 3.40 0.73
## [1450,] 15.0 29.0 0.99472 3.23 0.76
## [1451,] 15.0 28.0 0.99470 3.23 0.73
## [1452,] 8.0 17.0 0.99732 3.33 0.78
## [1453,] 37.0 53.0 0.99374 3.35 0.76
## [1454,] 27.0 85.0 0.99706 3.41 0.58
## [1455,] 7.0 23.0 0.99974 3.21 0.69
## [1456,] 9.0 17.0 0.99467 3.50 0.63
## [1457,] 38.0 89.0 0.99236 3.30 0.50
## [1458,] 27.0 85.0 0.99706 3.41 0.58
## [1459,] 8.0 20.0 0.99603 3.39 0.60
## [1460,] 7.0 15.0 0.99458 3.32 0.80
## [1461,] 34.0 64.0 0.99724 3.41 0.68
## [1462,] 6.0 13.0 0.99664 3.59 0.61
## [1463,] 14.0 31.0 0.99545 3.36 0.58
## [1464,] 14.0 39.0 0.99522 3.34 0.53
## [1465,] 34.0 53.0 0.99580 3.41 0.67
## [1466,] 34.0 53.0 0.99580 3.41 0.67
## [1467,] 31.0 54.0 0.99728 3.30 0.65
## [1468,] 19.0 32.0 0.99648 3.52 0.57
## [1469,] 31.0 54.0 0.99728 3.30 0.65
## [1470,] 20.0 49.0 0.99705 3.31 0.55
## [1471,] 8.0 24.0 0.99578 2.88 0.47
## [1472,] 8.0 16.0 0.99334 3.43 0.52
## [1473,] 23.0 44.0 0.99656 3.38 0.79
## [1474,] 14.0 45.0 0.99336 3.38 0.54
## [1475,] 48.0 82.0 1.00242 3.16 0.75
## [1476,] 16.0 89.0 0.99182 3.54 0.88
## [1477,] 48.0 82.0 1.00242 3.16 0.75
## [1478,] 16.0 89.0 0.99182 3.54 0.88
## [1479,] 3.0 14.0 0.99808 3.40 0.52
## [1480,] 10.0 22.0 0.99828 3.39 0.68
## [1481,] 6.0 13.0 0.99498 3.66 0.62
## [1482,] 10.0 22.0 0.99828 3.39 0.68
## [1483,] 3.0 9.0 0.99719 3.33 0.57
## [1484,] 6.0 12.0 0.99542 3.35 0.61
## [1485,] 4.0 11.0 0.99592 3.53 0.64
## [1486,] 8.0 25.0 0.99606 3.37 0.55
## [1487,] 9.0 23.0 0.99546 3.38 0.60
## [1488,] 9.0 17.0 0.99496 3.52 0.78
## [1489,] 5.0 13.0 0.99420 3.72 0.58
## [1490,] 4.0 11.0 0.99448 3.44 0.76
## [1491,] 8.0 18.0 0.99344 3.39 0.56
## [1492,] 5.0 13.0 0.99420 3.72 0.58
## [1493,] 6.0 18.0 0.99348 3.57 0.54
## [1494,] 23.0 147.0 0.99636 3.26 0.59
## [1495,] 15.0 28.0 0.99459 3.42 0.70
## [1496,] 15.0 28.0 0.99492 3.35 0.81
## [1497,] 23.0 147.0 0.99636 3.26 0.59
## [1498,] 7.0 16.0 0.99508 3.45 0.63
## [1499,] 7.0 13.0 0.99582 3.53 0.58
## [1500,] 7.0 16.0 0.99508 3.45 0.63
## [1501,] 8.0 15.0 0.99508 3.26 0.53
## [1502,] 21.0 64.0 0.99642 3.16 0.53
## [1503,] 15.0 60.0 0.99638 3.31 0.54
## [1504,] 6.0 14.0 0.99555 3.51 0.69
## [1505,] 5.0 12.0 0.99605 3.36 0.55
## [1506,] 6.0 12.0 0.99600 3.55 0.63
## [1507,] 6.0 14.0 0.99562 3.51 0.66
## [1508,] 5.0 12.0 0.99605 3.36 0.55
## [1509,] 17.0 25.0 0.99814 3.38 0.72
## [1510,] 7.0 20.0 0.99410 3.28 0.70
## [1511,] 26.0 48.0 0.99661 3.47 0.77
## [1512,] 19.0 27.0 0.99712 3.44 0.67
## [1513,] 11.0 17.0 0.99588 3.53 0.65
## [1514,] 17.0 65.0 0.99294 3.33 0.60
## [1515,] 16.0 65.0 0.99842 3.53 0.72
## [1516,] 16.0 65.0 0.99842 3.53 0.72
## [1517,] 23.0 58.0 0.99633 3.42 0.97
## [1518,] 29.0 44.0 0.99489 3.38 0.83
## [1519,] 7.0 20.0 0.99647 3.32 0.63
## [1520,] 14.0 27.0 0.99665 3.44 0.58
## [1521,] 29.0 44.0 0.99489 3.38 0.83
## [1522,] 9.0 23.0 0.99585 3.39 0.43
## [1523,] 23.0 58.0 0.99633 3.42 0.97
## [1524,] 29.0 61.0 0.99530 3.34 0.60
## [1525,] 22.0 47.0 0.99522 3.39 0.78
## [1526,] 18.0 34.0 0.99552 3.33 0.64
## [1527,] 18.0 38.0 0.99553 3.30 0.65
## [1528,] 15.0 24.0 0.99510 3.29 0.66
## [1529,] 21.0 59.0 0.99714 3.31 0.67
## [1530,] 42.0 52.0 0.99608 3.42 0.60
## [1531,] 9.0 18.0 0.99444 3.42 0.69
## [1532,] 13.0 28.0 0.99631 3.60 0.66
## [1533,] 18.0 22.0 0.99573 3.21 0.68
## [1534,] 32.0 84.0 0.99717 3.39 0.61
## [1535,] 13.0 29.0 0.99397 3.42 0.62
## [1536,] 15.0 35.0 0.99590 3.36 0.59
## [1537,] 24.0 45.0 0.99528 3.60 0.68
## [1538,] 20.0 31.0 0.99484 3.50 0.64
## [1539,] 15.0 26.0 0.99538 3.57 0.63
## [1540,] 34.0 60.0 0.99714 3.46 0.78
## [1541,] 11.0 32.0 0.99646 3.56 0.63
## [1542,] 19.0 49.0 0.99666 3.40 0.76
## [1543,] 29.0 38.0 0.99472 3.30 0.56
## [1544,] 14.0 19.0 0.99758 3.25 0.57
## [1545,] 12.0 19.0 0.99550 3.17 0.81
## [1546,] 16.0 28.0 0.99531 3.36 0.64
## [1547,] 17.0 26.0 0.99575 3.36 0.61
## [1548,] 12.0 26.0 0.99306 3.55 0.51
## [1549,] 19.0 50.0 0.99783 3.10 0.58
## [1550,] 17.0 24.0 0.99419 3.24 0.70
## [1551,] 17.0 26.0 0.99783 3.45 0.53
## [1552,] 18.0 27.0 0.99768 3.44 0.54
## [1553,] 32.0 54.0 0.99586 3.51 0.66
## [1554,] 18.0 28.0 0.99765 3.41 0.60
## [1555,] 15.0 23.0 0.99627 3.54 0.60
## [1556,] 15.0 24.0 0.99514 3.44 0.68
## [1557,] 12.0 20.0 0.99636 3.53 0.56
## [1558,] 15.0 23.0 0.99627 3.54 0.60
## [1559,] 66.0 115.0 0.99787 3.22 0.56
## [1560,] 31.0 131.0 0.99622 3.21 0.52
## [1561,] 31.0 131.0 0.99622 3.21 0.52
## [1562,] 31.0 131.0 0.99622 3.21 0.52
## [1563,] 12.0 20.0 0.99546 3.29 0.54
## [1564,] 12.0 20.0 0.99546 3.29 0.54
## [1565,] 12.0 20.0 0.99546 3.29 0.54
## [1566,] 26.0 42.0 0.99489 3.39 0.82
## [1567,] 24.0 52.0 0.99494 3.34 0.71
## [1568,] 12.0 20.0 0.99546 3.29 0.54
## [1569,] 25.0 42.0 0.99629 3.34 0.59
## [1570,] 15.0 34.0 0.99396 3.48 0.57
## [1571,] 19.0 35.0 0.99340 3.37 0.93
## [1572,] 15.0 25.0 0.99514 3.44 0.65
## [1573,] 35.0 104.0 0.99632 3.33 0.51
## [1574,] 15.0 50.0 0.99467 3.58 0.67
## [1575,] 23.0 92.0 0.99677 3.39 0.48
## [1576,] 12.0 20.0 0.99474 3.26 0.64
## [1577,] 16.0 29.0 0.99588 3.30 0.78
## [1578,] 13.0 27.0 0.99622 3.54 0.60
## [1579,] 13.0 20.0 0.99540 3.42 0.67
## [1580,] 24.0 32.0 0.99402 3.54 0.60
## [1581,] 9.0 26.0 0.99470 3.36 0.60
## [1582,] 24.0 32.0 0.99402 3.54 0.60
## [1583,] 13.0 27.0 0.99362 3.57 0.50
## [1584,] 32.0 98.0 0.99578 3.33 0.62
## [1585,] 24.0 34.0 0.99484 3.29 0.80
## [1586,] 22.0 48.0 0.99494 3.30 0.84
## [1587,] 34.0 60.0 0.99492 3.34 0.85
## [1588,] 18.0 28.0 0.99483 3.55 0.66
## [1589,] 34.0 102.0 0.99414 3.27 0.78
## [1590,] 29.0 79.0 0.99770 3.29 0.54
## [1591,] 26.0 35.0 0.99314 3.32 0.82
## [1592,] 16.0 26.0 0.99402 3.67 0.56
## [1593,] 29.0 40.0 0.99574 3.42 0.75
## [1594,] 28.0 38.0 0.99651 3.42 0.82
## [1595,] 32.0 44.0 0.99490 3.45 0.58
## [1596,] 39.0 51.0 0.99512 3.52 0.76
## [1597,] 29.0 40.0 0.99574 3.42 0.75
## [1598,] 32.0 44.0 0.99547 3.57 0.71
## [1599,] 18.0 42.0 0.99549 3.39 0.66
## alcohol quality
## [1,] 9.400000 0
## [2,] 9.800000 0
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## [1345,] 11.000000 0
## [1346,] 10.200000 1
## [1347,] 11.400000 0
## [1348,] 9.500000 0
## [1349,] 9.500000 0
## [1350,] 10.800000 0
## [1351,] 10.000000 0
## [1352,] 11.800000 1
## [1353,] 10.300000 0
## [1354,] 10.300000 0
## [1355,] 10.000000 0
## [1356,] 10.100000 0
## [1357,] 10.100000 0
## [1358,] 11.100000 1
## [1359,] 9.500000 0
## [1360,] 10.033333 1
## [1361,] 10.900000 0
## [1362,] 10.100000 0
## [1363,] 10.033333 1
## [1364,] 9.800000 0
## [1365,] 11.800000 1
## [1366,] 9.500000 0
## [1367,] 9.800000 0
## [1368,] 10.500000 1
## [1369,] 9.800000 1
## [1370,] 10.400000 0
## [1371,] 9.200000 0
## [1372,] 11.600000 1
## [1373,] 9.200000 0
## [1374,] 9.300000 0
## [1375,] 9.800000 0
## [1376,] 9.900000 0
## [1377,] 10.000000 0
## [1378,] 12.200000 1
## [1379,] 9.900000 1
## [1380,] 10.800000 1
## [1381,] 10.800000 1
## [1382,] 9.600000 0
## [1383,] 9.900000 0
## [1384,] 9.900000 0
## [1385,] 9.500000 0
## [1386,] 9.200000 0
## [1387,] 9.600000 0
## [1388,] 9.600000 0
## [1389,] 10.300000 0
## [1390,] 9.700000 0
## [1391,] 12.500000 1
## [1392,] 11.000000 0
## [1393,] 9.800000 0
## [1394,] 10.200000 0
## [1395,] 9.200000 0
## [1396,] 9.550000 1
## [1397,] 9.550000 0
## [1398,] 9.900000 0
## [1399,] 10.200000 1
## [1400,] 11.100000 1
## [1401,] 9.900000 0
## [1402,] 9.900000 0
## [1403,] 11.900000 1
## [1404,] 10.000000 1
## [1405,] 10.700000 1
## [1406,] 11.300000 1
## [1407,] 10.900000 1
## [1408,] 10.700000 1
## [1409,] 12.400000 1
## [1410,] 10.700000 1
## [1411,] 11.900000 1
## [1412,] 10.600000 1
## [1413,] 10.900000 1
## [1414,] 10.100000 0
## [1415,] 9.600000 0
## [1416,] 9.400000 0
## [1417,] 9.600000 0
## [1418,] 12.100000 1
## [1419,] 9.800000 0
## [1420,] 9.900000 0
## [1421,] 9.800000 0
## [1422,] 9.400000 0
## [1423,] 11.400000 1
## [1424,] 11.000000 0
## [1425,] 9.600000 1
## [1426,] 9.600000 1
## [1427,] 12.100000 1
## [1428,] 11.200000 0
## [1429,] 11.000000 0
## [1430,] 11.300000 0
## [1431,] 10.400000 0
## [1432,] 9.900000 1
## [1433,] 11.700000 1
## [1434,] 10.100000 1
## [1435,] 9.000000 1
## [1436,] 9.000000 1
## [1437,] 8.500000 0
## [1438,] 11.100000 0
## [1439,] 10.300000 0
## [1440,] 11.066667 1
## [1441,] 11.300000 1
## [1442,] 9.566667 1
## [1443,] 9.800000 0
## [1444,] 11.700000 0
## [1445,] 11.100000 1
## [1446,] 9.600000 1
## [1447,] 9.800000 0
## [1448,] 9.700000 0
## [1449,] 9.700000 0
## [1450,] 11.300000 1
## [1451,] 11.300000 1
## [1452,] 11.000000 1
## [1453,] 11.600000 1
## [1454,] 9.000000 0
## [1455,] 10.900000 1
## [1456,] 10.900000 1
## [1457,] 10.550000 1
## [1458,] 9.000000 0
## [1459,] 10.500000 0
## [1460,] 11.900000 1
## [1461,] 10.400000 1
## [1462,] 10.000000 0
## [1463,] 10.400000 1
## [1464,] 10.800000 1
## [1465,] 9.700000 0
## [1466,] 9.700000 0
## [1467,] 10.000000 1
## [1468,] 11.000000 0
## [1469,] 10.000000 1
## [1470,] 9.700000 0
## [1471,] 9.700000 0
## [1472,] 12.600000 0
## [1473,] 11.100000 1
## [1474,] 11.000000 0
## [1475,] 8.800000 0
## [1476,] 13.566667 1
## [1477,] 8.800000 0
## [1478,] 13.600000 1
## [1479,] 10.200000 0
## [1480,] 10.600000 0
## [1481,] 10.100000 0
## [1482,] 10.600000 0
## [1483,] 10.300000 0
## [1484,] 10.700000 0
## [1485,] 10.900000 0
## [1486,] 9.700000 0
## [1487,] 10.300000 0
## [1488,] 10.600000 0
## [1489,] 11.400000 0
## [1490,] 10.800000 1
## [1491,] 12.400000 1
## [1492,] 11.400000 0
## [1493,] 11.950000 0
## [1494,] 9.700000 0
## [1495,] 10.000000 1
## [1496,] 10.600000 1
## [1497,] 9.700000 0
## [1498,] 11.500000 1
## [1499,] 10.800000 1
## [1500,] 11.500000 1
## [1501,] 9.600000 0
## [1502,] 9.400000 0
## [1503,] 9.800000 0
## [1504,] 11.000000 1
## [1505,] 11.400000 1
## [1506,] 9.950000 0
## [1507,] 10.800000 1
## [1508,] 11.400000 1
## [1509,] 10.600000 1
## [1510,] 11.100000 0
## [1511,] 9.700000 1
## [1512,] 9.800000 0
## [1513,] 10.400000 1
## [1514,] 10.500000 1
## [1515,] 9.233333 1
## [1516,] 9.250000 1
## [1517,] 10.600000 0
## [1518,] 10.300000 1
## [1519,] 10.500000 0
## [1520,] 10.200000 0
## [1521,] 10.300000 1
## [1522,] 9.050000 0
## [1523,] 10.600000 0
## [1524,] 10.400000 0
## [1525,] 10.000000 1
## [1526,] 9.700000 0
## [1527,] 9.600000 1
## [1528,] 10.800000 1
## [1529,] 10.100000 1
## [1530,] 10.200000 1
## [1531,] 11.300000 1
## [1532,] 10.200000 0
## [1533,] 9.900000 1
## [1534,] 9.000000 0
## [1535,] 11.700000 1
## [1536,] 9.700000 1
## [1537,] 10.300000 1
## [1538,] 10.200000 1
## [1539,] 12.000000 0
## [1540,] 9.900000 0
## [1541,] 11.600000 1
## [1542,] 10.900000 1
## [1543,] 10.750000 1
## [1544,] 10.400000 1
## [1545,] 11.200000 1
## [1546,] 10.100000 1
## [1547,] 10.200000 0
## [1548,] 12.100000 0
## [1549,] 10.400000 0
## [1550,] 11.400000 1
## [1551,] 9.500000 0
## [1552,] 9.400000 0
## [1553,] 11.300000 1
## [1554,] 9.400000 0
## [1555,] 11.000000 1
## [1556,] 10.550000 1
## [1557,] 9.900000 0
## [1558,] 11.000000 1
## [1559,] 9.500000 0
## [1560,] 9.900000 0
## [1561,] 9.900000 0
## [1562,] 9.900000 0
## [1563,] 10.100000 0
## [1564,] 10.100000 0
## [1565,] 10.100000 0
## [1566,] 10.900000 1
## [1567,] 11.200000 1
## [1568,] 10.100000 0
## [1569,] 9.200000 0
## [1570,] 11.500000 1
## [1571,] 12.400000 1
## [1572,] 11.100000 1
## [1573,] 9.500000 0
## [1574,] 12.500000 1
## [1575,] 10.500000 1
## [1576,] 11.800000 1
## [1577,] 10.800000 1
## [1578,] 11.900000 1
## [1579,] 11.300000 1
## [1580,] 11.300000 0
## [1581,] 11.900000 1
## [1582,] 11.300000 0
## [1583,] 11.900000 0
## [1584,] 9.800000 0
## [1585,] 11.600000 1
## [1586,] 11.500000 1
## [1587,] 11.400000 1
## [1588,] 10.900000 1
## [1589,] 12.800000 1
## [1590,] 9.200000 0
## [1591,] 11.600000 1
## [1592,] 11.600000 1
## [1593,] 11.000000 1
## [1594,] 9.500000 1
## [1595,] 10.500000 0
## [1596,] 11.200000 1
## [1597,] 11.000000 1
## [1598,] 10.200000 0
## [1599,] 11.000000 1df_new_size <- floor(0.75 * nrow(df_new))
df_new_size## [1] 1199train_df_raw <- sample(nrow(df_new), size = df_new_size)
train_df_raw## [1] 1153 666 1332 960 976 1103 398 1482 1372 688 270 1085 461 1032
## [15] 859 1272 15 1335 725 25 890 1210 1025 54 1229 552 328 1447
## [29] 1359 1114 536 172 1360 197 132 697 968 470 975 670 56 607
## [43] 1204 842 83 96 1157 1100 646 832 1011 719 984 682 929 378
## [57] 350 541 518 232 1429 281 1129 1504 370 1295 1096 790 1022 1074
## [71] 1396 1492 36 1193 1099 312 671 1104 1252 673 1163 834 904 118
## [85] 791 877 754 42 479 180 435 260 173 931 251 1202 17 747
## [99] 202 1007 831 912 287 1018 622 70 1058 1364 512 395 386 357
## [113] 1437 121 388 1086 915 379 1135 325 1042 1166 733 907 1511 1213
## [127] 163 171 948 1217 26 252 772 338 1195 765 107 1562 302 1438
## [141] 582 964 12 368 181 1053 1269 458 327 1000 727 872 1506 799
## [155] 642 635 429 888 242 286 639 1279 1281 879 574 1197 506 813
## [169] 617 613 1251 489 1140 1089 1161 1055 1030 362 1459 187 459 480
## [183] 900 1278 523 1113 789 1505 1446 1170 418 658 669 1317 959 1526
## [197] 1430 703 1428 1508 1543 373 1487 1010 1242 672 843 730 988 640
## [211] 735 255 527 1274 1342 407 923 439 597 1044 1234 419 627 753
## [225] 1179 207 5 530 158 933 451 203 510 333 1107 292 1236 315
## [239] 1041 1111 1595 1431 139 1576 133 1150 299 268 1094 1473 718 785
## [253] 764 583 961 914 1091 1291 148 151 123 1199 740 114 1598 739
## [267] 244 932 779 729 428 1463 456 1203 1496 1594 661 1119 839 1424
## [281] 893 543 434 1258 997 272 1415 649 701 713 1577 409 178 1383
## [295] 1088 934 162 749 1017 971 771 1553 131 1589 1376 1369 1339 1315
## [309] 1154 230 528 1 291 1184 797 951 1043 186 524 850 1265 927
## [323] 317 590 1194 558 159 1326 1398 1171 1338 209 529 1256 483 1130
## [337] 1479 246 835 954 1343 400 1570 1268 1461 1401 803 1426 870 1226
## [351] 359 1239 1232 1513 1464 606 533 420 793 55 1031 1414 152 603
## [365] 1062 819 498 910 986 102 811 680 1285 1019 761 1451 972 1005
## [379] 444 1582 1520 678 1303 938 818 609 472 760 1470 469 1393 738
## [393] 695 1565 1050 946 1132 125 337 1034 1509 65 1247 1366 1391 1290
## [407] 956 852 873 199 1045 184 416 423 1527 1353 546 355 1123 517
## [421] 805 4 1263 374 221 599 1518 567 1214 1110 694 1439 1593 217
## [435] 999 1337 809 1425 1250 1106 415 905 1128 250 124 490 482 994
## [449] 442 165 1422 706 614 212 746 980 1141 258 1289 788 238 1521
## [463] 1535 777 601 1529 1257 1283 896 79 903 1379 537 18 993 371
## [477] 644 275 164 1008 33 269 99 32 1292 1467 1330 240 1456 372
## [491] 1040 485 1318 1083 686 955 1225 222 295 636 612 586 1200 505
## [505] 1477 296 331 311 769 966 1212 732 1026 1389 1006 676 1346 90
## [519] 1433 885 1441 1314 408 390 707 1293 34 981 129 726 862 542
## [533] 916 1380 320 1309 1556 846 1014 1186 942 1246 69 214 1260 812
## [547] 1574 1160 1056 1288 741 1357 241 224 256 1206 1362 1552 43 27
## [561] 745 1215 638 796 380 77 967 471 1162 344 705 1498 1253 783
## [575] 1245 1267 1532 229 926 681 1149 525 569 1403 257 692 716 1333
## [589] 1497 1302 1596 990 1409 1512 1037 261 643 901 109 845 1548 262
## [603] 193 74 183 882 665 1361 532 466 143 474 551 387 856 829
## [617] 1122 421 303 211 822 263 220 16 1373 1237 880 696 115 1546
## [631] 545 116 804 1109 191 276 393 1057 1537 693 1322 52 2 875
## [645] 513 1001 1159 970 1152 1133 548 762 704 674 1316 60 656 424
## [659] 1064 1176 867 626 427 145 1078 930 264 1566 780 699 1558 239
## [673] 1384 450 1387 297 1249 1191 329 1419 714 817 234 111 807 156
## [687] 401 492 675 82 206 1311 45 1523 1231 290 1580 1368 889 1564
## [701] 756 1444 1579 1334 1395 391 778 991 1435 213 235 462 1003 1510
## [715] 1235 113 1488 30 1287 909 1024 1331 1522 841 59 475 1294 436
## [729] 1329 406 556 432 1069 585 1583 547 392 816 602 1205 928 231
## [743] 285 318 662 1340 1411 267 1244 1375 974 496 794 884 1082 3
## [757] 660 616 1381 142 144 1363 720 1182 1450 965 953 1503 493 468
## [771] 578 935 782 677 736 1222 41 24 127 1542 277 1493 766 93
## [785] 731 1344 138 939 1408 98 767 918 1066 169 47 1148 652 1227
## [799] 1307 715 634 487 815 1284 10 87 871 1567 1116 690 684 998
## [813] 618 721 488 1077 1060 1349 800 467 808 1072 1328 1432 431 1137
## [827] 535 833 855 1449 1407 1560 1301 743 1485 1052 1220 1146 1177 945
## [841] 1412 369 987 310 717 911 49 1474 580 947 464 67 1266 375
## [855] 414 301 330 293 1304 610 919 1178 1423 1209 46 100 97 1139
## [869] 1296 1586 1551 215 1402 1185 160 1201 897 1134 364 61 863 495
## [883] 848 881 363 161 394 58 353 63 1483 854 1142 1554 1015 1046
## [897] 837 1115 104 595 40 92 1039 969 1224 279 342 1105 441 253
## [911] 1098 902 44 1027 508 581 1536 563 9 28 306 1588 218 313
## [925] 1093 892 334 1539 1519 784 307 763 1489 1458 141 1319 810 1392
## [939] 770 1051 80 473 1465 51 1164 62 1370 1491 1455 858 1399 465
## [953] 1581 1499 1156 1276 322 117 445 1299 1306 298 150 1514 1028 860
## [967] 308 1531 853 1144 1547 577 950 265 383 514 906 365 66 1469
## [981] 119 126 1471 1495 576 1404 1173 557 201 588 977 1068 625 1174
## [995] 112 502 973 438 540 22 1172 820 196 204 922 14 1262 1320
## [1009] 478 526 274 376 522 1336 345 336 1118 1241 849 806 283 657
## [1023] 500 326 57 1021 1507 1126 1038 7 1181 568 358 210 985 457
## [1037] 631 1065 1533 641 346 166 1460 936 1075 1033 645 447 560 39
## [1051] 691 1145 1584 1151 278 382 81 615 1388 1386 775 78 136 35
## [1065] 1382 134 154 886 604 1208 11 377 1341 343 122 1591 830 584
## [1079] 1599 1466 1125 1324 1016 1563 553 1013 20 332 587 481 623 667
## [1093] 68 1081 861 453 1555 448 319 624 1073 1348 340 1243 170 1454
## [1107] 1084 949 1561 1453 1108 600 1397 352 399 1297 650 1264 1478 88
## [1121] 593 226 1472 1049 1012 499 679 1462 147 1484 827 844 130 38
## [1135] 1187 651 571 821 596 384 402 1036 894 1347 75 1196 723 266
## [1149] 1321 237 722 385 575 570 1350 94 659 996 724 1198 228 944
## [1163] 561 153 1572 1500 742 1480 1300 1475 1305 71 629 190 1443 1528
## [1177] 786 48 188 403 1095 1175 1541 1009 865 1192 1575 748 683 979
## [1191] 324 289 397 177 1271 605 309 655 95train_raw.df <- as.data.frame(df_new[train_df_raw, ])
train_raw.df## fixed acidity volatile acidity citric acid residual sugar chlorides
## 1 8.3 0.600 0.25 2.20 0.118
## 2 9.4 0.590 0.14 2.00 0.084
## 3 7.8 0.870 0.26 3.80 0.107
## 4 8.0 0.590 0.05 2.00 0.089
## 5 7.2 0.410 0.30 2.10 0.083
## 6 6.1 0.480 0.09 1.70 0.078
## 7 11.5 0.590 0.59 2.60 0.087
## 8 8.2 0.280 0.60 3.00 0.104
## 9 7.5 0.580 0.56 3.10 0.153
## 10 9.1 0.640 0.23 3.10 0.095
## 11 11.5 0.180 0.51 4.00 0.104
## 12 7.2 0.380 0.30 1.80 0.073
## 13 9.2 0.430 0.52 2.30 0.083
## 14 7.3 0.550 0.01 1.80 0.093
## 15 11.7 0.280 0.47 1.70 0.054
## 16 7.3 0.440 0.20 1.60 0.049
## 17 8.9 0.620 0.18 3.80 0.176
## 18 7.2 0.835 0.00 2.00 0.166
## 19 7.5 1.115 0.10 3.10 0.086
## 20 6.9 0.400 0.14 2.40 0.085
## 21 10.7 0.900 0.34 6.60 0.112
## 22 6.2 0.390 0.43 2.00 0.071
## 23 7.7 0.580 0.01 1.80 0.088
## 24 8.6 0.380 0.36 3.00 0.081
## 25 5.1 0.420 0.00 1.80 0.044
## 26 9.4 0.430 0.24 2.80 0.092
## 27 10.3 0.440 0.50 4.50 0.107
## 28 6.9 0.630 0.02 1.90 0.078
## 29 7.4 0.640 0.17 5.40 0.168
## 30 8.9 0.240 0.39 1.60 0.074
## 31 9.1 0.220 0.24 2.10 0.078
## 32 8.0 0.420 0.17 2.00 0.073
## 33 11.6 0.475 0.40 1.40 0.091
## 34 7.3 0.580 0.30 2.40 0.074
## 35 5.6 0.500 0.09 2.30 0.049
## 36 7.0 0.650 0.02 2.10 0.066
## 37 8.5 0.660 0.20 2.10 0.097
## 38 8.7 0.820 0.02 1.20 0.070
## 39 8.8 0.330 0.41 5.90 0.073
## 40 11.3 0.340 0.45 2.00 0.082
## 41 7.7 0.620 0.04 3.80 0.084
## 42 9.4 0.410 0.48 4.60 0.072
## 43 9.9 0.740 0.19 5.80 0.111
## 44 6.6 0.660 0.00 3.00 0.115
## 45 7.4 0.500 0.47 2.00 0.086
## 46 4.7 0.600 0.17 2.30 0.058
## 47 8.5 0.180 0.51 1.75 0.071
## 48 8.6 0.520 0.38 1.50 0.096
## 49 7.8 0.640 0.10 6.00 0.115
## 50 5.9 0.610 0.08 2.10 0.071
## 51 8.9 0.280 0.45 1.70 0.067
## 52 8.4 0.560 0.04 2.00 0.082
## 53 9.1 0.500 0.30 1.90 0.065
## 54 8.0 0.450 0.23 2.20 0.094
## 55 8.6 0.470 0.27 2.30 0.055
## 56 9.4 0.270 0.53 2.40 0.074
## 57 9.1 0.785 0.00 2.60 0.093
## 58 9.2 0.590 0.24 3.30 0.101
## 59 10.4 0.610 0.49 2.10 0.200
## 60 8.0 0.380 0.06 1.80 0.078
## 61 7.8 0.640 0.00 1.90 0.072
## 62 11.4 0.260 0.44 3.60 0.071
## 63 10.0 0.430 0.33 2.70 0.095
## 64 6.2 0.440 0.39 2.50 0.077
## 65 9.4 0.270 0.53 2.40 0.074
## 66 8.2 0.635 0.10 2.10 0.073
## 67 9.4 0.400 0.47 2.50 0.087
## 68 8.6 0.630 0.17 2.90 0.099
## 69 11.3 0.360 0.66 2.40 0.123
## 70 8.0 0.620 0.33 2.70 0.088
## 71 8.6 0.685 0.10 1.60 0.092
## 72 5.6 0.540 0.04 1.70 0.049
## 73 7.8 0.645 0.00 5.50 0.086
## 74 7.2 0.250 0.37 2.50 0.063
## 75 8.0 0.310 0.45 2.10 0.216
## 76 7.9 0.530 0.24 2.00 0.072
## 77 6.9 0.400 0.24 2.50 0.083
## 78 7.4 0.490 0.27 2.10 0.071
## 79 7.5 0.580 0.14 2.20 0.077
## 80 9.8 1.240 0.34 2.00 0.079
## 81 8.5 0.320 0.42 2.30 0.075
## 82 11.6 0.470 0.44 1.60 0.147
## 83 6.8 0.590 0.06 6.00 0.060
## 84 7.8 0.560 0.12 2.00 0.082
## 85 7.4 0.370 0.43 2.60 0.082
## 86 7.1 0.470 0.00 2.20 0.067
## 87 8.3 0.650 0.10 2.90 0.089
## 88 8.8 0.610 0.30 2.80 0.088
## 89 9.6 0.680 0.24 2.20 0.087
## 90 8.8 0.610 0.14 2.40 0.067
## 91 10.4 0.410 0.55 3.20 0.076
## 92 10.0 0.310 0.47 2.60 0.085
## 93 8.0 0.420 0.17 2.00 0.073
## 94 6.6 0.610 0.01 1.90 0.080
## 95 10.8 0.320 0.44 1.60 0.063
## 96 7.9 0.340 0.36 1.90 0.065
## 97 8.5 0.280 0.56 1.80 0.092
## 98 8.2 0.340 0.38 2.50 0.080
## 99 8.8 0.370 0.48 2.10 0.097
## 100 9.1 0.290 0.33 2.05 0.063
## 101 7.5 0.685 0.07 2.50 0.058
## 102 9.1 0.280 0.46 9.00 0.114
## 103 12.0 0.450 0.55 2.00 0.073
## 104 8.0 0.180 0.37 0.90 0.049
## 105 8.2 0.560 0.23 3.40 0.078
## 106 8.0 0.705 0.05 1.90 0.074
## 107 7.6 0.420 0.25 3.90 0.104
## 108 8.0 0.830 0.27 2.00 0.080
## 109 10.0 0.590 0.31 2.20 0.090
## 110 12.7 0.600 0.65 2.30 0.063
## 111 7.4 0.630 0.07 2.40 0.090
## 112 11.5 0.410 0.52 3.00 0.080
## 113 10.0 0.380 0.38 1.60 0.169
## 114 7.3 1.070 0.09 1.70 0.178
## 115 8.3 0.660 0.15 1.90 0.079
## 116 6.8 0.480 0.08 1.80 0.074
## 117 7.3 0.305 0.39 1.20 0.059
## 118 11.4 0.625 0.66 6.20 0.088
## 119 8.5 0.280 0.35 1.70 0.061
## 120 10.0 0.490 0.20 11.00 0.071
## 121 6.9 0.490 0.19 1.70 0.079
## 122 8.5 0.440 0.50 1.90 0.369
## 123 7.3 0.835 0.03 2.10 0.092
## 124 7.2 0.540 0.27 2.60 0.084
## 125 6.4 0.360 0.21 2.20 0.047
## 126 6.8 0.650 0.02 2.10 0.078
## 127 7.8 0.530 0.04 1.70 0.076
## 128 7.9 0.885 0.03 1.80 0.058
## 129 8.3 0.280 0.48 2.10 0.093
## 130 7.9 0.570 0.31 2.00 0.079
## 131 6.3 0.390 0.16 1.40 0.080
## 132 7.1 0.600 0.00 1.80 0.074
## 133 9.4 0.685 0.26 2.40 0.082
## 134 7.8 0.430 0.32 2.80 0.080
## 135 7.0 0.745 0.12 1.80 0.114
## 136 9.1 0.680 0.11 2.80 0.093
## 137 7.8 0.410 0.68 1.70 0.467
## 138 7.8 0.600 0.26 2.00 0.080
## 139 11.1 0.180 0.48 1.50 0.068
## 140 6.8 0.915 0.29 4.80 0.070
## 141 12.3 0.500 0.49 2.20 0.089
## 142 8.8 0.270 0.39 2.00 0.100
## 143 7.5 0.500 0.36 6.10 0.071
## 144 10.4 0.575 0.61 2.60 0.076
## 145 8.8 0.610 0.14 2.40 0.067
## 146 5.6 0.605 0.05 2.40 0.073
## 147 6.9 0.410 0.31 2.00 0.079
## 148 9.2 0.630 0.21 2.70 0.097
## 149 11.6 0.530 0.66 3.65 0.121
## 150 6.4 0.690 0.00 1.65 0.055
## 151 8.1 0.720 0.09 2.80 0.084
## 152 6.9 0.560 0.03 1.50 0.086
## 153 6.7 0.760 0.02 1.80 0.078
## 154 9.4 0.500 0.34 3.60 0.082
## 155 9.5 0.590 0.44 2.30 0.071
## 156 7.9 0.350 0.21 1.90 0.073
## 157 9.1 0.520 0.33 1.30 0.070
## 158 10.7 0.520 0.38 2.60 0.066
## 159 12.0 0.380 0.56 2.10 0.093
## 160 9.9 0.590 0.07 3.40 0.102
## 161 7.7 0.915 0.12 2.20 0.143
## 162 8.0 0.715 0.22 2.30 0.075
## 163 7.1 0.460 0.20 1.90 0.077
## 164 8.8 0.610 0.19 4.00 0.094
## 165 10.5 0.590 0.49 2.10 0.070
## 166 7.9 0.580 0.23 2.30 0.076
## 167 10.2 0.490 0.63 2.90 0.072
## 168 10.8 0.450 0.33 2.50 0.099
## 169 9.6 0.600 0.50 2.30 0.079
## 170 7.5 0.640 0.00 2.40 0.077
## 171 7.1 0.600 0.01 2.30 0.079
## 172 11.6 0.320 0.55 2.80 0.081
## 173 8.2 0.510 0.24 2.00 0.079
## 174 11.6 0.410 0.54 1.50 0.095
## 175 10.6 0.360 0.57 2.30 0.087
## 176 8.2 0.640 0.27 2.00 0.095
## 177 7.7 0.580 0.01 1.80 0.088
## 178 8.6 0.450 0.31 2.60 0.086
## 179 8.4 0.290 0.40 1.70 0.067
## 180 7.4 0.390 0.48 2.00 0.082
## 181 10.4 0.330 0.63 2.80 0.084
## 182 9.4 0.685 0.11 2.70 0.077
## 183 8.3 1.020 0.02 3.40 0.084
## 184 7.0 0.690 0.00 1.90 0.114
## 185 8.2 0.390 0.49 2.30 0.099
## 186 7.9 0.330 0.41 1.50 0.056
## 187 10.0 0.560 0.24 2.20 0.079
## 188 7.5 0.380 0.57 2.30 0.106
## 189 7.4 0.785 0.19 5.20 0.094
## 190 7.6 0.500 0.29 2.30 0.086
## 191 7.0 0.580 0.12 1.90 0.091
## 192 12.0 0.500 0.59 1.40 0.073
## 193 10.0 0.730 0.43 2.30 0.059
## 194 5.4 0.740 0.00 1.20 0.041
## 195 6.4 0.570 0.12 2.30 0.120
## 196 6.7 0.480 0.08 2.10 0.064
## 197 7.9 0.180 0.40 2.20 0.049
## 198 7.0 0.640 0.02 2.10 0.067
## 199 7.6 0.410 0.33 2.50 0.078
## 200 7.5 0.380 0.57 2.30 0.106
## 201 6.7 0.855 0.02 1.90 0.064
## 202 9.1 0.280 0.48 1.80 0.067
## 203 6.8 0.680 0.21 2.10 0.070
## 204 9.6 0.500 0.36 2.80 0.116
## 205 9.8 0.370 0.39 2.50 0.079
## 206 8.2 0.730 0.21 1.70 0.074
## 207 10.6 0.500 0.45 2.60 0.119
## 208 6.4 0.865 0.03 3.20 0.071
## 209 7.1 0.360 0.30 1.60 0.080
## 210 8.9 0.290 0.35 1.90 0.067
## 211 7.7 0.560 0.20 2.00 0.075
## 212 7.1 0.600 0.00 1.80 0.074
## 213 7.3 0.365 0.49 2.50 0.088
## 214 7.5 0.580 0.20 2.00 0.073
## 215 7.5 0.510 0.02 1.70 0.084
## 216 9.8 0.440 0.47 2.50 0.063
## 217 8.4 0.620 0.12 1.80 0.072
## 218 10.4 0.410 0.55 3.20 0.076
## 219 12.4 0.400 0.51 2.00 0.059
## 220 9.5 0.390 0.41 8.90 0.069
## 221 10.2 0.230 0.37 2.20 0.057
## 222 11.9 0.380 0.51 2.00 0.121
## 223 8.8 0.600 0.29 2.20 0.098
## 224 7.6 0.540 0.13 2.50 0.097
## 225 5.6 0.915 0.00 2.10 0.041
## 226 12.8 0.300 0.74 2.60 0.095
## 227 7.4 0.700 0.00 1.90 0.076
## 228 9.9 0.630 0.24 2.40 0.077
## 229 7.1 0.430 0.42 5.50 0.071
## 230 7.6 0.400 0.29 1.90 0.078
## 231 11.9 0.390 0.69 2.80 0.095
## 232 6.8 0.500 0.11 1.50 0.075
## 233 13.3 0.290 0.75 2.80 0.084
## 234 8.0 0.580 0.28 3.20 0.066
## 235 8.2 0.230 0.42 1.90 0.069
## 236 11.0 0.200 0.48 2.00 0.343
## 237 6.0 0.330 0.32 12.90 0.054
## 238 7.4 0.360 0.29 2.60 0.087
## 239 7.4 0.965 0.00 2.20 0.088
## 240 8.3 0.530 0.00 1.40 0.070
## 241 6.2 0.600 0.08 2.00 0.090
## 242 7.4 0.410 0.24 1.80 0.066
## 243 7.8 0.560 0.19 2.10 0.081
## 244 7.5 0.520 0.40 2.20 0.060
## 245 5.6 0.500 0.09 2.30 0.049
## 246 10.0 0.350 0.47 2.00 0.061
## 247 7.2 0.650 0.02 2.30 0.094
## 248 7.9 0.350 0.46 3.60 0.078
## 249 9.2 0.310 0.36 2.20 0.079
## 250 7.6 0.350 0.60 2.60 0.073
## 251 7.6 0.460 0.11 2.60 0.079
## 252 7.1 0.590 0.01 2.50 0.077
## 253 9.3 0.655 0.26 2.00 0.096
## 254 11.7 0.490 0.49 2.20 0.083
## 255 8.5 0.470 0.27 1.90 0.058
## 256 9.4 0.395 0.46 4.60 0.094
## 257 10.0 0.260 0.54 1.90 0.083
## 258 7.6 0.740 0.00 1.90 0.100
## 259 7.6 0.490 0.26 1.60 0.236
## 260 7.3 0.330 0.47 2.10 0.077
## 261 7.3 0.695 0.00 2.50 0.075
## 262 7.7 0.260 0.26 2.00 0.052
## 263 9.0 0.690 0.00 2.40 0.088
## 264 10.1 0.310 0.44 2.30 0.080
## 265 5.9 0.645 0.12 2.00 0.075
## 266 9.0 0.460 0.23 2.80 0.092
## 267 15.0 0.210 0.44 2.20 0.075
## 268 7.4 0.610 0.01 2.00 0.074
## 269 8.3 0.430 0.30 3.40 0.079
## 270 6.4 0.570 0.02 1.80 0.067
## 271 9.5 0.780 0.22 1.90 0.077
## 272 6.8 0.640 0.03 2.30 0.075
## 273 11.3 0.620 0.67 5.20 0.086
## 274 8.6 0.420 0.39 1.80 0.068
## 275 7.0 0.430 0.02 1.90 0.080
## 276 6.8 0.620 0.08 1.90 0.068
## 277 7.2 0.520 0.07 1.40 0.074
## 278 7.1 0.390 0.12 2.10 0.065
## 279 10.1 0.310 0.35 1.60 0.075
## 280 6.4 0.530 0.09 3.90 0.123
## 281 10.1 0.450 0.23 1.90 0.082
## 282 9.3 0.715 0.24 2.10 0.070
## 283 12.3 0.390 0.63 2.30 0.091
## 284 7.0 0.580 0.28 4.80 0.085
## 285 5.6 0.660 0.00 2.20 0.087
## 286 11.5 0.180 0.51 4.00 0.104
## 287 10.0 0.320 0.59 2.20 0.077
## 288 8.7 0.480 0.30 2.80 0.066
## 289 10.6 1.020 0.43 2.90 0.076
## 290 8.7 0.690 0.00 3.20 0.084
## 291 8.0 0.300 0.63 1.60 0.081
## 292 10.4 0.340 0.58 3.70 0.174
## 293 7.5 0.520 0.42 2.30 0.087
## 294 8.0 0.600 0.22 2.10 0.080
## 295 7.9 0.190 0.42 1.60 0.057
## 296 7.4 0.610 0.01 2.00 0.074
## 297 7.6 0.680 0.02 1.30 0.072
## 298 7.2 0.500 0.18 2.10 0.071
## 299 8.9 0.380 0.40 2.20 0.068
## 300 10.4 0.260 0.48 1.90 0.066
## 301 7.1 0.590 0.02 2.30 0.082
## 302 6.3 0.680 0.01 3.70 0.103
## 303 8.0 0.745 0.56 2.00 0.118
## 304 7.2 0.660 0.33 2.50 0.068
## 305 7.2 0.560 0.26 2.00 0.083
## 306 8.0 0.770 0.32 2.10 0.079
## 307 6.0 0.500 0.00 1.40 0.057
## 308 7.0 0.360 0.21 2.40 0.086
## 309 9.6 0.420 0.35 2.10 0.083
## 310 6.9 0.520 0.25 2.60 0.081
## 311 7.0 0.380 0.49 2.50 0.097
## 312 7.4 0.700 0.00 1.90 0.076
## 313 8.7 0.520 0.09 2.50 0.091
## 314 6.8 0.660 0.07 1.60 0.070
## 315 8.7 0.460 0.31 2.50 0.126
## 316 8.9 0.120 0.45 1.80 0.075
## 317 8.9 0.500 0.21 2.20 0.088
## 318 8.9 0.310 0.57 2.00 0.111
## 319 9.3 0.400 0.49 2.50 0.085
## 320 6.4 0.630 0.21 1.60 0.080
## 321 8.5 0.370 0.32 1.80 0.066
## 322 9.4 0.240 0.33 2.30 0.061
## 323 9.6 0.560 0.23 3.40 0.102
## 324 10.2 0.290 0.49 2.60 0.059
## 325 6.4 0.885 0.00 2.30 0.166
## 326 15.6 0.645 0.49 4.20 0.095
## 327 7.1 0.680 0.00 2.20 0.073
## 328 6.7 0.460 0.24 1.70 0.077
## 329 7.3 0.590 0.26 2.00 0.080
## 330 9.2 0.360 0.34 1.60 0.062
## 331 6.0 0.500 0.00 1.40 0.057
## 332 7.8 0.440 0.28 2.70 0.100
## 333 8.2 0.420 0.49 2.60 0.084
## 334 6.1 0.640 0.02 2.40 0.069
## 335 10.6 0.360 0.59 2.20 0.152
## 336 10.5 0.430 0.35 3.30 0.092
## 337 7.1 0.875 0.05 5.70 0.082
## 338 7.3 0.660 0.00 2.00 0.084
## 339 8.8 0.685 0.26 1.60 0.088
## 340 10.2 0.340 0.48 2.10 0.052
## 341 7.6 0.540 0.02 1.70 0.085
## 342 8.7 0.765 0.22 2.30 0.064
## 343 6.2 0.510 0.14 1.90 0.056
## 344 10.4 0.430 0.50 2.30 0.068
## 345 6.4 0.420 0.09 2.30 0.054
## 346 7.9 0.690 0.21 2.10 0.080
## 347 5.1 0.585 0.00 1.70 0.044
## 348 8.3 0.260 0.37 1.40 0.076
## 349 7.6 0.630 0.03 2.00 0.080
## 350 9.2 0.460 0.23 2.60 0.091
## 351 11.9 0.430 0.66 3.10 0.109
## 352 8.1 0.730 0.00 2.50 0.081
## 353 7.8 0.815 0.01 2.60 0.074
## 354 6.4 0.790 0.04 2.20 0.061
## 355 6.9 0.630 0.01 2.40 0.076
## 356 8.3 0.600 0.13 2.60 0.085
## 357 11.9 0.380 0.49 2.70 0.098
## 358 6.8 0.770 0.00 1.80 0.066
## 359 7.1 0.610 0.02 2.50 0.081
## 360 7.6 0.510 0.15 2.80 0.110
## 361 7.1 0.590 0.00 2.10 0.091
## 362 9.9 0.570 0.25 2.00 0.104
## 363 9.2 0.520 1.00 3.40 0.610
## 364 7.7 0.835 0.00 2.60 0.081
## 365 9.1 0.400 0.50 1.80 0.071
## 366 7.1 0.715 0.00 2.35 0.071
## 367 7.2 0.340 0.32 2.50 0.090
## 368 7.3 0.305 0.39 1.20 0.059
## 369 7.4 0.580 0.00 2.00 0.064
## 370 7.8 0.500 0.30 1.90 0.075
## 371 7.3 0.490 0.10 2.60 0.068
## 372 10.8 0.260 0.45 3.30 0.060
## 373 7.0 0.420 0.19 2.30 0.071
## 374 8.0 0.180 0.37 0.90 0.049
## 375 9.0 0.580 0.25 2.80 0.075
## 376 7.2 0.370 0.32 2.00 0.062
## 377 10.4 0.260 0.48 1.90 0.066
## 378 8.2 0.430 0.29 1.60 0.081
## 379 10.0 0.440 0.49 2.70 0.077
## 380 6.2 0.560 0.09 1.70 0.053
## 381 6.6 0.700 0.08 2.60 0.106
## 382 8.6 0.800 0.11 2.30 0.084
## 383 9.1 0.370 0.32 2.10 0.064
## 384 12.0 0.630 0.50 1.40 0.071
## 385 10.8 0.290 0.42 1.60 0.084
## 386 10.1 0.650 0.37 5.10 0.110
## 387 9.6 0.540 0.42 2.40 0.081
## 388 8.8 0.420 0.21 2.50 0.092
## 389 7.3 0.980 0.05 2.10 0.061
## 390 11.4 0.360 0.69 2.10 0.090
## 391 7.1 0.620 0.06 1.30 0.070
## 392 8.2 0.590 0.00 2.50 0.093
## 393 9.0 0.470 0.31 2.70 0.084
## 394 7.2 0.695 0.13 2.00 0.076
## 395 9.3 0.500 0.36 1.80 0.084
## 396 10.2 0.440 0.42 2.00 0.071
## 397 5.9 0.190 0.21 1.70 0.045
## 398 7.8 0.500 0.17 1.60 0.082
## 399 8.9 0.430 0.45 1.90 0.052
## 400 7.5 0.570 0.08 2.60 0.089
## 401 7.1 0.270 0.60 2.10 0.074
## 402 7.2 0.725 0.05 4.65 0.086
## 403 7.4 0.740 0.07 1.70 0.086
## 404 7.8 0.500 0.09 2.20 0.115
## 405 6.0 0.490 0.00 2.30 0.068
## 406 7.0 0.600 0.30 4.50 0.068
## 407 8.5 0.210 0.52 1.90 0.090
## 408 9.3 0.430 0.44 1.90 0.085
## 409 7.3 0.350 0.24 2.00 0.067
## 410 5.4 0.835 0.08 1.20 0.046
## 411 6.4 0.390 0.33 3.30 0.046
## 412 6.8 0.610 0.20 1.80 0.077
## 413 8.6 0.725 0.24 6.60 0.117
## 414 7.7 0.960 0.20 2.00 0.047
## 415 6.8 0.470 0.08 2.20 0.064
## 416 7.6 0.645 0.03 1.90 0.086
## 417 9.1 0.470 0.49 2.60 0.094
## 418 6.1 0.210 0.40 1.40 0.066
## 419 6.3 0.470 0.00 1.40 0.055
## 420 12.5 0.600 0.49 4.30 0.100
## 421 8.4 0.520 0.22 2.70 0.084
## 422 11.2 0.280 0.56 1.90 0.075
## 423 9.8 0.450 0.38 2.50 0.081
## 424 7.4 0.550 0.22 2.20 0.106
## 425 7.8 0.340 0.37 2.00 0.082
## 426 8.5 0.585 0.18 2.10 0.078
## 427 6.5 0.530 0.06 2.00 0.063
## 428 8.7 0.700 0.24 2.50 0.226
## 429 9.6 0.380 0.42 1.90 0.071
## 430 10.8 0.470 0.43 2.10 0.171
## 431 9.0 0.480 0.32 2.80 0.084
## 432 7.0 0.590 0.00 1.70 0.052
## 433 6.3 0.510 0.13 2.30 0.076
## 434 8.7 0.625 0.16 2.00 0.101
## 435 8.9 0.840 0.34 1.40 0.050
## 436 6.0 0.500 0.00 1.40 0.057
## 437 7.4 0.530 0.12 1.90 0.165
## 438 8.3 0.260 0.37 1.40 0.076
## 439 7.1 0.600 0.01 2.30 0.079
## 440 6.3 0.570 0.28 2.10 0.048
## 441 8.8 0.520 0.34 2.70 0.087
## 442 6.8 0.590 0.06 6.00 0.060
## 443 6.3 0.760 0.00 2.90 0.072
## 444 7.3 0.660 0.00 2.00 0.084
## 445 8.0 0.710 0.00 2.60 0.080
## 446 9.3 0.390 0.40 2.60 0.073
## 447 9.4 0.300 0.56 2.80 0.080
## 448 7.1 0.340 0.28 2.00 0.082
## 449 11.9 0.400 0.65 2.15 0.068
## 450 7.3 0.590 0.26 7.20 0.070
## 451 7.5 0.400 0.18 1.60 0.079
## 452 8.4 1.035 0.15 6.00 0.073
## 453 8.2 0.390 0.38 1.50 0.058
## 454 8.0 0.725 0.24 2.80 0.083
## 455 7.3 0.510 0.18 2.10 0.070
## 456 12.2 0.450 0.49 1.40 0.075
## 457 7.3 0.400 0.30 1.70 0.080
## 458 6.6 0.695 0.00 2.10 0.075
## 459 7.0 0.600 0.30 4.50 0.068
## 460 10.0 0.560 0.24 2.20 0.079
## 461 7.2 0.645 0.00 1.90 0.097
## 462 6.5 0.530 0.06 2.00 0.063
## 463 6.6 0.560 0.14 2.40 0.064
## 464 6.9 0.765 0.18 2.40 0.243
## 465 8.2 0.915 0.27 2.10 0.088
## 466 7.9 0.290 0.49 2.20 0.096
## 467 7.5 0.590 0.22 1.80 0.082
## 468 7.9 0.765 0.00 2.00 0.084
## 469 7.1 0.590 0.01 2.30 0.080
## 470 6.7 0.750 0.12 2.00 0.086
## 471 7.4 0.635 0.10 2.40 0.080
## 472 7.2 0.450 0.15 2.00 0.078
## 473 9.9 0.630 0.24 2.40 0.077
## 474 8.1 0.560 0.28 1.70 0.368
## 475 6.5 0.400 0.10 2.00 0.076
## 476 6.9 0.765 0.02 2.30 0.063
## 477 9.5 0.590 0.44 2.30 0.071
## 478 7.5 0.650 0.18 7.00 0.088
## 479 7.4 0.600 0.26 7.30 0.070
## 480 9.1 0.300 0.34 2.00 0.064
## 481 8.3 0.655 0.12 2.30 0.083
## 482 6.9 0.540 0.04 3.00 0.077
## 483 7.6 0.900 0.06 2.50 0.079
## 484 6.9 0.685 0.00 2.50 0.105
## 485 8.2 0.635 0.10 2.10 0.073
## 486 7.3 0.480 0.32 2.10 0.062
## 487 7.4 0.600 0.26 2.10 0.083
## 488 8.2 1.000 0.09 2.30 0.065
## 489 6.5 0.900 0.00 1.60 0.052
## 490 7.9 0.240 0.40 1.60 0.056
## 491 8.9 0.500 0.21 2.20 0.088
## 492 10.6 0.440 0.68 4.10 0.114
## 493 9.9 0.440 0.46 2.20 0.091
## 494 7.2 0.380 0.30 1.80 0.073
## 495 8.1 0.780 0.23 2.60 0.059
## 496 7.6 0.430 0.40 2.70 0.082
## 497 12.6 0.390 0.49 2.50 0.080
## 498 7.4 0.530 0.26 2.00 0.101
## 499 13.3 0.340 0.52 3.20 0.094
## 500 8.7 0.840 0.00 1.40 0.065
## 501 13.2 0.380 0.55 2.70 0.081
## 502 7.6 0.510 0.24 2.40 0.091
## 503 7.9 0.580 0.23 2.30 0.076
## 504 10.5 0.240 0.42 1.80 0.077
## 505 9.9 0.500 0.50 13.80 0.205
## 506 10.8 0.500 0.46 2.50 0.073
## 507 10.2 0.360 0.64 2.90 0.122
## 508 10.3 0.530 0.48 2.50 0.063
## 509 7.1 0.590 0.02 2.30 0.082
## 510 8.3 0.340 0.40 2.40 0.065
## 511 6.6 0.440 0.15 2.10 0.076
## 512 8.9 0.875 0.13 3.45 0.088
## 513 8.6 0.830 0.00 2.80 0.095
## 514 6.6 0.640 0.31 6.10 0.083
## 515 6.8 0.360 0.32 1.80 0.067
## 516 9.3 0.410 0.39 2.20 0.064
## 517 8.2 0.440 0.24 2.30 0.063
## 518 7.0 0.620 0.08 1.80 0.076
## 519 5.9 0.440 0.00 1.60 0.042
## 520 8.8 0.610 0.19 4.00 0.094
## 521 7.2 0.370 0.32 2.00 0.062
## 522 7.0 0.360 0.21 2.30 0.086
## 523 12.0 0.390 0.66 3.00 0.093
## 524 9.6 0.380 0.31 2.50 0.096
## 525 7.0 0.780 0.08 2.00 0.093
## 526 5.9 0.395 0.13 2.40 0.056
## 527 6.9 0.605 0.12 10.70 0.073
## 528 9.1 0.500 0.30 1.90 0.065
## 529 8.0 0.590 0.16 1.80 0.065
## 530 9.0 0.660 0.17 3.00 0.077
## 531 5.8 1.010 0.66 2.00 0.039
## 532 9.5 0.460 0.49 6.30 0.064
## 533 8.6 0.315 0.40 2.20 0.079
## 534 7.5 0.570 0.02 2.60 0.077
## 535 9.6 0.770 0.12 2.90 0.082
## 536 9.7 0.690 0.32 2.50 0.088
## 537 7.0 0.560 0.17 1.70 0.065
## 538 6.4 0.640 0.21 1.80 0.081
## 539 7.5 0.710 0.00 1.60 0.092
## 540 7.0 0.430 0.30 2.00 0.085
## 541 9.9 0.270 0.49 5.00 0.082
## 542 7.4 0.550 0.19 1.80 0.082
## 543 9.3 0.320 0.57 2.00 0.074
## 544 8.2 0.570 0.26 2.20 0.060
## 545 6.8 0.640 0.00 2.70 0.123
## 546 12.9 0.500 0.55 2.80 0.072
## 547 6.0 0.580 0.20 2.40 0.075
## 548 10.2 0.410 0.43 2.20 0.110
## 549 8.2 0.640 0.27 2.00 0.095
## 550 8.0 0.600 0.08 2.60 0.056
## 551 8.3 0.760 0.29 4.20 0.075
## 552 6.1 0.340 0.25 1.80 0.084
## 553 8.9 0.635 0.37 1.70 0.263
## 554 8.6 0.645 0.25 2.00 0.083
## 555 8.0 0.570 0.23 3.20 0.073
## 556 7.2 0.360 0.46 2.10 0.074
## 557 8.3 0.850 0.14 2.50 0.093
## 558 7.1 0.670 0.00 2.30 0.083
## 559 7.5 0.490 0.20 2.60 0.332
## 560 7.6 0.410 0.24 1.80 0.080
## 561 11.1 0.390 0.54 2.70 0.095
## 562 10.2 0.330 0.46 1.90 0.081
## 563 9.5 0.885 0.27 2.30 0.084
## 564 10.8 0.890 0.30 2.60 0.132
## 565 8.3 0.420 0.38 2.50 0.094
## 566 8.8 0.410 0.64 2.20 0.093
## 567 9.0 0.380 0.41 2.40 0.103
## 568 13.0 0.320 0.65 2.60 0.093
## 569 8.8 0.450 0.43 1.40 0.076
## 570 10.9 0.390 0.47 1.80 0.118
## 571 9.1 0.765 0.04 1.60 0.078
## 572 6.9 0.740 0.03 2.30 0.054
## 573 7.1 0.720 0.00 1.80 0.123
## 574 9.0 0.820 0.05 2.40 0.081
## 575 5.9 0.290 0.25 13.40 0.067
## 576 7.2 0.570 0.05 2.30 0.081
## 577 6.1 0.705 0.10 2.80 0.081
## 578 7.7 0.430 0.25 2.60 0.073
## 579 8.6 0.220 0.36 1.90 0.064
## 580 13.3 0.430 0.58 1.90 0.070
## 581 7.8 0.390 0.42 2.00 0.086
## 582 9.2 0.430 0.49 2.40 0.086
## 583 9.8 0.500 0.49 2.60 0.250
## 584 7.6 0.300 0.42 2.00 0.052
## 585 9.4 0.340 0.37 2.20 0.075
## 586 9.2 0.920 0.24 2.60 0.087
## 587 7.2 0.490 0.18 2.70 0.069
## 588 8.4 0.390 0.10 1.70 0.075
## 589 7.7 0.540 0.26 1.90 0.089
## 590 6.7 0.860 0.07 2.00 0.100
## 591 5.9 0.550 0.10 2.20 0.062
## 592 9.7 0.295 0.40 1.50 0.073
## 593 8.1 0.290 0.36 2.20 0.048
## 594 7.1 0.690 0.04 2.10 0.068
## 595 7.6 0.310 0.34 2.50 0.082
## 596 7.9 0.330 0.23 1.70 0.077
## 597 9.9 0.540 0.45 2.30 0.071
## 598 8.9 0.310 0.36 2.60 0.056
## 599 8.0 0.330 0.53 2.50 0.091
## 600 9.9 0.250 0.46 1.70 0.062
## 601 6.3 0.600 0.10 1.60 0.048
## 602 7.0 0.975 0.04 2.00 0.087
## 603 6.8 0.630 0.12 3.80 0.099
## 604 8.3 0.675 0.26 2.10 0.084
## 605 7.2 0.730 0.02 2.50 0.076
## 606 7.6 0.715 0.00 2.10 0.068
## 607 12.1 0.400 0.52 2.00 0.092
## 608 9.2 0.540 0.31 2.30 0.112
## 609 11.9 0.380 0.49 2.70 0.098
## 610 10.0 0.290 0.40 2.90 0.098
## 611 5.2 0.340 0.00 1.80 0.050
## 612 9.9 0.350 0.55 2.10 0.062
## 613 6.8 0.510 0.01 2.10 0.074
## 614 7.8 0.540 0.26 2.00 0.088
## 615 7.6 0.735 0.02 2.50 0.071
## 616 7.8 0.570 0.09 2.30 0.065
## 617 6.6 0.500 0.00 1.80 0.062
## 618 9.5 0.560 0.33 2.40 0.089
## 619 8.3 0.705 0.12 2.60 0.092
## 620 9.7 0.530 0.60 2.00 0.039
## 621 4.9 0.420 0.00 2.10 0.048
## 622 8.0 0.520 0.03 1.70 0.070
## 623 7.8 0.530 0.33 2.40 0.080
## 624 8.9 0.620 0.19 3.90 0.170
## 625 8.7 0.780 0.51 1.70 0.415
## 626 7.8 0.550 0.00 1.70 0.070
## 627 7.2 0.600 0.04 2.50 0.076
## 628 5.1 0.470 0.02 1.30 0.034
## 629 7.8 0.560 0.19 1.80 0.104
## 630 6.5 0.630 0.33 1.80 0.059
## 631 14.3 0.310 0.74 1.80 0.075
## 632 9.4 0.400 0.31 2.20 0.090
## 633 7.7 0.560 0.08 2.50 0.114
## 634 8.1 0.780 0.10 3.30 0.090
## 635 8.2 0.500 0.35 2.90 0.077
## 636 7.9 0.545 0.06 4.00 0.087
## 637 9.5 0.370 0.52 2.00 0.082
## 638 8.9 0.480 0.53 4.00 0.101
## 639 6.1 0.530 0.08 1.90 0.077
## 640 8.6 0.490 0.51 2.00 0.422
## 641 5.0 0.740 0.00 1.20 0.041
## 642 6.6 0.520 0.04 2.20 0.069
## 643 7.8 0.880 0.00 2.60 0.098
## 644 10.4 0.380 0.46 2.10 0.104
## 645 10.7 0.400 0.48 2.10 0.125
## 646 7.5 0.430 0.30 2.20 0.062
## 647 6.7 0.410 0.43 2.80 0.076
## 648 6.7 0.560 0.09 2.90 0.079
## 649 6.1 0.580 0.23 2.50 0.044
## 650 7.4 0.360 0.34 1.80 0.075
## 651 10.6 0.310 0.49 2.50 0.067
## 652 9.3 0.655 0.26 2.00 0.096
## 653 7.5 0.380 0.48 2.60 0.073
## 654 8.2 0.730 0.21 1.70 0.074
## 655 7.5 0.630 0.27 2.00 0.083
## 656 7.3 0.390 0.31 2.40 0.074
## 657 9.7 0.550 0.17 2.90 0.087
## 658 10.5 0.240 0.47 2.10 0.066
## 659 10.2 0.290 0.65 2.40 0.075
## 660 6.5 0.610 0.00 2.20 0.095
## 661 6.8 0.490 0.22 2.30 0.071
## 662 6.8 0.690 0.00 5.60 0.124
## 663 6.4 0.670 0.08 2.10 0.045
## 664 5.2 0.340 0.00 1.80 0.050
## 665 8.6 0.370 0.65 6.40 0.080
## 666 8.7 0.330 0.38 3.30 0.063
## 667 7.9 0.370 0.23 1.80 0.077
## 668 6.7 0.670 0.02 1.90 0.061
## 669 7.1 0.520 0.03 2.60 0.076
## 670 9.4 0.615 0.28 3.20 0.087
## 671 6.6 0.855 0.02 2.40 0.062
## 672 7.2 0.630 0.00 1.90 0.097
## 673 8.0 0.600 0.22 2.10 0.080
## 674 11.9 0.390 0.69 2.80 0.095
## 675 7.4 0.640 0.07 1.80 0.100
## 676 10.6 0.830 0.37 2.60 0.086
## 677 6.9 0.410 0.33 2.20 0.081
## 678 9.1 0.400 0.57 4.60 0.080
## 679 13.4 0.270 0.62 2.60 0.082
## 680 7.8 0.530 0.01 1.60 0.077
## 681 8.0 0.430 0.36 2.30 0.075
## 682 9.8 0.510 0.19 3.20 0.081
## 683 6.9 0.520 0.25 2.60 0.081
## 684 7.8 0.560 0.19 1.80 0.104
## 685 8.4 0.250 0.39 2.00 0.041
## 686 7.1 0.430 0.42 5.50 0.071
## 687 6.6 0.735 0.02 7.90 0.122
## 688 9.2 0.410 0.50 2.50 0.055
## 689 10.8 0.400 0.41 2.20 0.084
## 690 7.8 0.430 0.70 1.90 0.464
## 691 12.8 0.300 0.74 2.60 0.095
## 692 7.5 0.610 0.26 1.90 0.073
## 693 6.8 0.670 0.02 1.80 0.050
## 694 6.1 0.320 0.25 2.30 0.071
## 695 7.7 0.180 0.34 2.70 0.066
## 696 11.6 0.420 0.53 3.30 0.105
## 697 6.2 0.560 0.09 1.70 0.053
## 698 6.9 0.540 0.30 2.20 0.088
## 699 6.8 0.570 0.00 2.50 0.072
## 700 7.2 0.695 0.13 2.00 0.076
## 701 7.8 0.910 0.07 1.90 0.058
## 702 6.9 0.580 0.20 1.75 0.058
## 703 6.8 0.670 0.15 1.80 0.118
## 704 9.1 0.775 0.22 2.20 0.079
## 705 6.4 0.570 0.14 3.90 0.070
## 706 5.6 0.850 0.05 1.40 0.045
## 707 6.9 0.635 0.17 2.40 0.241
## 708 7.7 0.390 0.12 1.70 0.097
## 709 10.2 0.540 0.37 15.40 0.214
## 710 11.6 0.440 0.64 2.10 0.059
## 711 8.2 1.000 0.09 2.30 0.065
## 712 8.3 0.615 0.22 2.60 0.087
## 713 9.1 0.290 0.33 2.05 0.063
## 714 7.9 0.180 0.40 1.80 0.062
## 715 7.1 0.750 0.01 2.20 0.059
## 716 8.4 0.600 0.10 2.20 0.085
## 717 6.0 0.640 0.05 1.90 0.066
## 718 7.8 0.645 0.00 2.00 0.082
## 719 7.1 0.160 0.44 2.50 0.068
## 720 7.4 0.520 0.13 2.40 0.078
## 721 8.2 0.320 0.42 2.30 0.098
## 722 7.4 0.600 0.26 2.10 0.083
## 723 6.9 0.480 0.20 1.90 0.082
## 724 11.1 0.420 0.47 2.65 0.085
## 725 7.8 0.590 0.18 2.30 0.076
## 726 10.5 0.280 0.51 1.70 0.080
## 727 7.5 0.755 0.00 1.90 0.084
## 728 12.3 0.390 0.63 2.30 0.091
## 729 6.5 0.520 0.11 1.80 0.073
## 730 8.7 0.310 0.46 1.40 0.059
## 731 15.5 0.645 0.49 4.20 0.095
## 732 7.8 0.550 0.35 2.20 0.074
## 733 11.1 0.310 0.53 2.20 0.060
## 734 11.8 0.330 0.49 3.40 0.093
## 735 6.1 0.715 0.10 2.60 0.053
## 736 7.5 0.550 0.24 2.00 0.078
## 737 13.7 0.415 0.68 2.90 0.085
## 738 10.8 0.450 0.33 2.50 0.099
## 739 13.2 0.460 0.52 2.20 0.071
## 740 7.2 0.360 0.46 2.10 0.074
## 741 8.4 0.670 0.19 2.20 0.093
## 742 5.2 0.480 0.04 1.60 0.054
## 743 9.9 0.590 0.07 3.40 0.102
## 744 9.6 0.770 0.12 2.90 0.082
## 745 7.5 0.420 0.31 1.60 0.080
## 746 7.5 0.510 0.02 1.70 0.084
## 747 6.6 0.960 0.00 1.80 0.082
## 748 8.1 0.870 0.00 3.30 0.096
## 749 8.3 0.560 0.22 2.40 0.082
## 750 6.8 0.815 0.00 1.20 0.267
## 751 8.5 0.340 0.44 1.70 0.079
## 752 10.7 0.350 0.53 2.60 0.070
## 753 7.7 0.600 0.00 2.60 0.055
## 754 7.2 0.600 0.04 2.50 0.076
## 755 7.9 0.300 0.68 8.30 0.050
## 756 7.8 0.760 0.04 2.30 0.092
## 757 7.1 0.840 0.02 4.40 0.096
## 758 9.6 0.600 0.50 2.30 0.079
## 759 7.5 0.570 0.02 2.60 0.077
## 760 8.3 0.715 0.15 1.80 0.089
## 761 6.3 0.390 0.08 1.70 0.066
## 762 11.6 0.475 0.40 1.40 0.091
## 763 7.1 0.660 0.00 3.90 0.086
## 764 9.8 0.390 0.43 1.65 0.068
## 765 7.2 0.380 0.31 2.00 0.056
## 766 8.5 0.470 0.27 1.90 0.058
## 767 8.2 0.310 0.40 2.20 0.058
## 768 7.3 0.585 0.18 2.40 0.078
## 769 8.9 0.400 0.51 2.60 0.052
## 770 8.8 0.460 0.45 2.60 0.065
## 771 8.8 0.440 0.49 2.80 0.083
## 772 6.6 0.610 0.01 1.90 0.080
## 773 6.5 0.460 0.14 2.40 0.114
## 774 10.8 0.400 0.41 2.20 0.084
## 775 7.7 0.965 0.10 2.10 0.112
## 776 10.9 0.320 0.52 1.80 0.132
## 777 7.3 0.450 0.36 5.90 0.074
## 778 8.5 0.490 0.11 2.30 0.084
## 779 8.2 1.330 0.00 1.70 0.081
## 780 7.4 0.250 0.29 2.20 0.054
## 781 6.9 0.540 0.04 3.00 0.077
## 782 6.2 0.650 0.06 1.60 0.050
## 783 9.2 0.670 0.10 3.00 0.091
## 784 8.6 0.490 0.29 2.00 0.110
## 785 9.5 0.550 0.66 2.30 0.387
## 786 7.5 0.510 0.02 1.70 0.084
## 787 7.2 0.415 0.36 2.00 0.081
## 788 7.2 0.380 0.38 2.80 0.068
## 789 6.0 0.510 0.00 2.10 0.064
## 790 7.0 0.500 0.25 2.00 0.070
## 791 8.8 0.590 0.18 2.90 0.089
## 792 6.8 0.410 0.31 8.80 0.084
## 793 7.7 0.610 0.18 2.40 0.083
## 794 6.8 0.630 0.07 2.10 0.089
## 795 7.7 0.935 0.43 2.20 0.114
## 796 10.0 0.410 0.45 6.20 0.071
## 797 9.8 0.880 0.25 2.50 0.104
## 798 7.5 0.580 0.03 4.10 0.080
## 799 9.7 0.690 0.32 2.50 0.088
## 800 9.9 0.740 0.28 2.60 0.078
## 801 10.1 0.935 0.22 3.40 0.105
## 802 10.2 0.670 0.39 1.90 0.054
## 803 12.6 0.410 0.54 2.80 0.103
## 804 8.7 0.630 0.28 2.70 0.096
## 805 7.5 0.500 0.36 6.10 0.071
## 806 8.6 0.490 0.28 1.90 0.110
## 807 7.7 0.715 0.01 2.10 0.064
## 808 6.7 0.160 0.64 2.10 0.059
## 809 7.0 0.690 0.07 2.50 0.091
## 810 8.1 0.380 0.48 1.80 0.157
## 811 8.1 0.780 0.23 2.60 0.059
## 812 5.6 0.660 0.00 2.20 0.087
## 813 11.5 0.310 0.51 2.20 0.079
## 814 8.4 0.560 0.04 2.00 0.082
## 815 10.2 0.645 0.36 1.80 0.053
## 816 9.9 0.320 0.56 2.00 0.073
## 817 8.9 0.480 0.53 4.00 0.101
## 818 7.2 0.655 0.03 1.80 0.078
## 819 9.4 0.500 0.34 3.60 0.082
## 820 10.3 0.500 0.42 2.00 0.069
## 821 8.2 0.280 0.40 2.40 0.052
## 822 7.5 0.770 0.20 8.10 0.098
## 823 6.7 0.460 0.24 1.70 0.077
## 824 7.6 0.430 0.31 2.10 0.069
## 825 10.5 0.240 0.47 2.10 0.066
## 826 10.4 0.520 0.45 2.00 0.080
## 827 10.0 0.480 0.24 2.70 0.102
## 828 10.4 0.440 0.42 1.50 0.145
## 829 9.3 0.360 0.39 1.50 0.080
## 830 6.9 0.580 0.01 1.90 0.080
## 831 8.2 0.240 0.34 5.10 0.062
## 832 7.8 0.600 0.26 2.00 0.080
## 833 5.2 0.645 0.00 2.15 0.080
## 834 6.5 0.615 0.00 1.90 0.065
## 835 6.8 0.910 0.06 2.00 0.060
## 836 8.5 0.460 0.59 1.40 0.414
## 837 9.0 0.390 0.40 1.30 0.044
## 838 8.2 0.200 0.43 2.50 0.076
## 839 6.5 0.880 0.03 5.60 0.079
## 840 8.3 0.300 0.49 3.80 0.090
## 841 6.4 0.470 0.40 2.40 0.071
## 842 10.3 0.340 0.52 2.80 0.159
## 843 9.8 0.340 0.39 1.40 0.066
## 844 7.4 0.290 0.38 1.70 0.062
## 845 8.0 0.430 0.36 2.30 0.075
## 846 9.3 0.380 0.48 3.80 0.132
## 847 6.4 0.400 0.23 1.60 0.066
## 848 6.1 0.600 0.08 1.80 0.071
## 849 10.6 0.310 0.49 2.20 0.063
## 850 10.2 0.440 0.58 4.10 0.092
## 851 8.1 0.660 0.70 2.20 0.098
## 852 7.5 0.520 0.11 1.50 0.079
## 853 7.2 0.570 0.05 2.30 0.081
## 854 14.0 0.410 0.63 3.80 0.089
## 855 9.9 0.400 0.53 6.70 0.097
## 856 7.5 0.530 0.06 2.60 0.086
## 857 10.7 0.460 0.39 2.00 0.061
## 858 10.4 0.550 0.23 2.70 0.091
## 859 8.0 0.280 0.44 1.80 0.081
## 860 6.3 0.360 0.19 3.20 0.075
## 861 8.4 0.360 0.32 2.20 0.081
## 862 7.1 0.660 0.00 2.40 0.052
## 863 7.0 0.540 0.00 2.10 0.079
## 864 7.2 0.360 0.46 2.10 0.074
## 865 4.6 0.520 0.15 2.10 0.054
## 866 8.1 0.545 0.18 1.90 0.080
## 867 6.8 0.775 0.00 3.00 0.102
## 868 7.5 0.410 0.15 3.70 0.104
## 869 6.6 0.630 0.00 4.30 0.093
## 870 7.2 0.390 0.44 2.60 0.066
## 871 7.1 0.680 0.00 2.30 0.087
## 872 7.8 0.735 0.08 2.40 0.092
## 873 7.9 0.690 0.21 2.10 0.080
## 874 6.7 0.640 0.23 2.10 0.080
## 875 6.8 0.600 0.18 1.90 0.079
## 876 7.7 0.570 0.21 1.50 0.069
## 877 8.3 0.310 0.39 2.40 0.078
## 878 7.2 0.480 0.07 5.50 0.089
## 879 12.5 0.460 0.63 2.00 0.071
## 880 8.8 0.400 0.40 2.20 0.079
## 881 7.5 0.420 0.32 2.70 0.067
## 882 6.5 0.390 0.23 8.30 0.051
## 883 7.4 0.680 0.16 1.80 0.078
## 884 9.2 0.560 0.18 1.60 0.078
## 885 11.9 0.580 0.66 2.50 0.072
## 886 7.6 0.950 0.03 2.00 0.090
## 887 8.4 0.665 0.61 2.00 0.112
## 888 7.5 0.630 0.12 5.10 0.111
## 889 7.7 0.665 0.00 2.40 0.090
## 890 7.5 0.520 0.16 1.90 0.085
## 891 7.2 0.580 0.54 2.10 0.114
## 892 9.3 0.360 0.39 1.50 0.080
## 893 8.2 0.380 0.32 2.50 0.080
## 894 7.3 0.735 0.00 2.20 0.080
## 895 8.0 0.580 0.16 2.00 0.120
## 896 6.9 0.440 0.00 1.40 0.070
## 897 6.7 0.280 0.28 2.40 0.012
## 898 5.0 0.400 0.50 4.30 0.046
## 899 8.1 0.575 0.22 2.10 0.077
## 900 7.9 0.720 0.17 2.60 0.096
## 901 7.3 0.450 0.36 5.90 0.074
## 902 8.6 0.490 0.28 1.90 0.110
## 903 8.7 0.410 0.41 6.20 0.078
## 904 9.0 0.400 0.43 2.40 0.068
## 905 10.5 0.360 0.47 2.20 0.074
## 906 10.3 0.320 0.45 6.40 0.073
## 907 10.6 0.420 0.48 2.70 0.065
## 908 8.0 0.480 0.34 2.20 0.073
## 909 12.6 0.310 0.72 2.20 0.072
## 910 11.1 0.350 0.48 3.10 0.090
## 911 8.6 0.520 0.38 1.50 0.096
## 912 7.4 0.635 0.10 2.40 0.080
## 913 8.1 0.660 0.22 2.20 0.069
## 914 7.9 0.310 0.32 1.90 0.066
## 915 11.2 0.670 0.55 2.30 0.084
## 916 12.3 0.500 0.49 2.20 0.089
## 917 7.0 0.550 0.13 2.20 0.075
## 918 9.0 0.540 0.49 2.90 0.094
## 919 7.8 0.580 0.02 2.00 0.073
## 920 7.9 0.430 0.21 1.60 0.106
## 921 10.3 0.530 0.48 2.50 0.063
## 922 5.8 0.610 0.11 1.80 0.066
## 923 8.1 0.725 0.22 2.20 0.072
## 924 9.0 0.460 0.31 2.80 0.093
## 925 7.0 0.540 0.09 2.00 0.081
## 926 7.2 0.660 0.03 2.30 0.078
## 927 8.4 0.560 0.08 2.10 0.105
## 928 6.2 0.640 0.09 2.50 0.081
## 929 7.4 0.470 0.46 2.20 0.114
## 930 6.5 0.460 0.14 2.40 0.114
## 931 7.6 0.620 0.32 2.20 0.082
## 932 8.8 0.700 0.00 1.70 0.069
## 933 5.6 0.540 0.04 1.70 0.049
## 934 7.6 0.490 0.33 1.90 0.074
## 935 8.4 0.745 0.11 1.90 0.090
## 936 7.5 0.630 0.27 2.00 0.083
## 937 7.6 0.480 0.31 2.80 0.070
## 938 8.0 0.640 0.22 2.40 0.094
## 939 7.9 0.720 0.01 1.90 0.076
## 940 7.1 0.430 0.17 1.80 0.082
## 941 8.3 0.625 0.20 1.50 0.080
## 942 12.5 0.370 0.55 2.60 0.083
## 943 6.8 0.590 0.10 1.70 0.063
## 944 8.8 0.660 0.26 1.70 0.074
## 945 9.0 0.785 0.24 1.70 0.078
## 946 7.7 0.690 0.49 1.80 0.115
## 947 6.6 0.610 0.00 1.60 0.069
## 948 7.1 0.220 0.49 1.80 0.039
## 949 11.7 0.450 0.63 2.20 0.073
## 950 8.2 0.260 0.34 2.50 0.073
## 951 7.0 0.600 0.12 2.20 0.083
## 952 11.5 0.315 0.54 2.10 0.084
## 953 7.4 0.350 0.33 2.40 0.068
## 954 6.6 0.895 0.04 2.30 0.068
## 955 8.3 0.600 0.25 2.20 0.118
## 956 8.0 0.715 0.22 2.30 0.075
## 957 9.3 0.610 0.26 3.40 0.090
## 958 8.3 0.540 0.28 1.90 0.077
## 959 5.3 0.570 0.01 1.70 0.054
## 960 5.7 0.600 0.00 1.40 0.063
## 961 7.5 0.610 0.26 1.90 0.073
## 962 7.1 0.630 0.06 2.00 0.083
## 963 8.2 0.400 0.44 2.80 0.089
## 964 6.4 0.560 0.15 1.80 0.078
## 965 6.4 0.795 0.00 2.20 0.065
## 966 6.8 0.560 0.22 1.80 0.074
## 967 10.3 0.410 0.42 2.40 0.213
## 968 6.6 0.440 0.09 2.20 0.063
## 969 8.0 0.420 0.32 2.50 0.080
## 970 7.0 0.220 0.30 1.80 0.065
## 971 7.0 0.570 0.02 2.00 0.072
## 972 9.9 0.500 0.24 2.30 0.103
## 973 8.9 0.120 0.45 1.80 0.075
## 974 12.5 0.560 0.49 2.40 0.064
## 975 8.3 0.260 0.42 2.00 0.080
## 976 10.5 0.510 0.64 2.40 0.107
## 977 9.2 0.580 0.20 3.00 0.081
## 978 12.8 0.615 0.66 5.80 0.083
## 979 7.2 0.725 0.05 4.65 0.086
## 980 7.3 0.480 0.32 2.10 0.062
## 981 8.8 0.550 0.04 2.20 0.119
## 982 9.0 0.620 0.04 1.90 0.146
## 983 10.0 0.690 0.11 1.40 0.084
## 984 6.4 0.310 0.09 1.40 0.066
## 985 12.3 0.270 0.49 3.10 0.079
## 986 7.2 0.330 0.33 1.70 0.061
## 987 9.7 0.420 0.46 2.10 0.074
## 988 10.9 0.530 0.49 4.60 0.118
## 989 9.6 0.320 0.47 1.40 0.056
## 990 7.3 0.730 0.24 1.90 0.108
## 991 7.2 0.410 0.30 2.10 0.083
## 992 11.1 0.310 0.53 2.20 0.060
## 993 6.8 0.690 0.00 5.60 0.124
## 994 7.6 0.360 0.31 1.70 0.079
## 995 8.4 0.620 0.09 2.20 0.084
## 996 10.4 0.440 0.73 6.55 0.074
## 997 10.1 0.380 0.50 2.40 0.104
## 998 11.1 0.450 0.73 3.20 0.066
## 999 11.2 0.500 0.74 5.15 0.100
## 1000 7.6 0.390 0.31 2.30 0.082
## 1001 7.1 0.590 0.00 2.20 0.078
## 1002 9.1 0.660 0.15 3.20 0.097
## 1003 7.8 0.590 0.33 2.00 0.074
## 1004 7.0 0.420 0.35 1.60 0.088
## 1005 8.4 0.360 0.32 2.20 0.081
## 1006 7.8 0.610 0.29 1.60 0.114
## 1007 6.3 1.020 0.00 2.00 0.083
## 1008 9.1 0.760 0.68 1.70 0.414
## 1009 10.4 0.240 0.49 1.80 0.075
## 1010 10.4 0.640 0.24 2.80 0.105
## 1011 8.4 0.715 0.20 2.40 0.076
## 1012 11.5 0.540 0.71 4.40 0.124
## 1013 7.6 0.410 0.49 2.00 0.088
## 1014 6.6 0.580 0.02 2.40 0.069
## 1015 11.9 0.570 0.50 2.60 0.082
## 1016 11.9 0.695 0.53 3.40 0.128
## 1017 7.0 0.690 0.07 2.50 0.091
## 1018 7.5 0.610 0.20 1.70 0.076
## 1019 6.4 0.640 0.21 1.80 0.081
## 1020 8.2 0.280 0.40 2.40 0.052
## 1021 7.6 0.520 0.12 3.00 0.067
## 1022 10.7 0.430 0.39 2.20 0.106
## 1023 8.7 0.690 0.31 3.00 0.086
## 1024 10.0 0.490 0.20 11.00 0.071
## 1025 10.2 0.420 0.57 3.40 0.070
## 1026 11.3 0.360 0.66 2.40 0.123
## 1027 6.8 0.810 0.05 2.00 0.070
## 1028 8.8 0.240 0.35 1.70 0.055
## 1029 7.3 0.910 0.10 1.80 0.074
## 1030 7.9 0.600 0.06 1.60 0.069
## 1031 8.2 0.350 0.33 2.40 0.076
## 1032 8.7 0.700 0.24 2.50 0.226
## 1033 10.5 0.420 0.66 2.95 0.116
## 1034 11.0 0.300 0.58 2.10 0.054
## 1035 12.2 0.450 0.49 1.40 0.075
## 1036 8.9 0.590 0.39 2.30 0.095
## 1037 8.7 0.540 0.26 2.50 0.097
## 1038 8.2 0.740 0.09 2.00 0.067
## 1039 7.2 0.530 0.13 2.00 0.058
## 1040 9.9 0.540 0.45 2.30 0.071
## 1041 7.0 0.685 0.00 1.90 0.067
## 1042 7.8 0.630 0.48 1.70 0.100
## 1043 7.9 0.200 0.35 1.70 0.054
## 1044 8.8 0.300 0.38 2.30 0.060
## 1045 7.5 0.770 0.20 8.10 0.098
## 1046 8.1 0.820 0.00 4.10 0.095
## 1047 9.9 0.540 0.45 2.30 0.071
## 1048 12.5 0.380 0.60 2.60 0.081
## 1049 13.0 0.470 0.49 4.30 0.085
## 1050 5.7 1.130 0.09 1.50 0.172
## 1051 7.4 1.185 0.00 4.25 0.097
## 1052 7.3 0.320 0.23 2.30 0.066
## 1053 6.2 0.460 0.29 2.10 0.074
## 1054 8.2 0.330 0.32 2.80 0.067
## 1055 11.5 0.180 0.51 4.00 0.104
## 1056 13.7 0.415 0.68 2.90 0.085
## 1057 6.2 0.450 0.20 1.60 0.069
## 1058 9.2 0.755 0.18 2.20 0.148
## 1059 7.4 0.640 0.07 1.80 0.100
## 1060 8.0 0.810 0.25 3.40 0.076
## 1061 7.9 0.400 0.30 1.80 0.157
## 1062 6.8 0.785 0.00 2.40 0.104
## 1063 8.4 0.745 0.11 1.90 0.090
## 1064 5.2 0.320 0.25 1.80 0.103
## 1065 6.8 0.830 0.09 1.80 0.074
## 1066 6.6 0.500 0.01 1.50 0.060
## 1067 7.5 0.600 0.03 1.80 0.095
## 1068 8.9 0.750 0.14 2.50 0.086
## 1069 13.2 0.460 0.52 2.20 0.071
## 1070 9.9 0.720 0.55 1.70 0.136
## 1071 6.7 0.580 0.08 1.80 0.097
## 1072 11.5 0.450 0.50 3.00 0.078
## 1073 7.5 0.510 0.02 1.70 0.084
## 1074 10.9 0.390 0.47 1.80 0.118
## 1075 8.8 0.550 0.04 2.20 0.119
## 1076 6.3 0.550 0.15 1.80 0.077
## 1077 5.9 0.610 0.08 2.10 0.071
## 1078 12.0 0.280 0.49 1.90 0.074
## 1079 6.0 0.310 0.47 3.60 0.067
## 1080 6.8 0.590 0.10 1.70 0.063
## 1081 6.5 0.580 0.00 2.20 0.096
## 1082 9.1 0.360 0.39 1.80 0.060
## 1083 10.5 0.390 0.46 2.20 0.075
## 1084 7.2 0.695 0.13 2.00 0.076
## 1085 9.5 0.460 0.24 2.70 0.092
## 1086 7.7 1.005 0.15 2.10 0.102
## 1087 7.9 0.320 0.51 1.80 0.341
## 1088 10.2 0.360 0.64 2.90 0.122
## 1089 11.1 0.310 0.49 2.70 0.094
## 1090 10.6 0.280 0.39 15.50 0.069
## 1091 10.0 0.580 0.22 1.90 0.080
## 1092 8.3 0.490 0.36 1.80 0.222
## 1093 6.6 0.705 0.07 1.60 0.076
## 1094 10.3 0.270 0.56 1.40 0.047
## 1095 7.2 0.620 0.06 2.70 0.077
## 1096 6.8 0.560 0.03 1.70 0.084
## 1097 6.6 0.855 0.02 2.40 0.062
## 1098 9.3 0.480 0.29 2.10 0.127
## 1099 9.8 0.660 0.39 3.20 0.083
## 1100 7.9 0.510 0.25 2.90 0.077
## 1101 7.2 0.350 0.26 1.80 0.083
## 1102 7.2 0.655 0.03 1.80 0.078
## 1103 12.5 0.280 0.54 2.30 0.082
## 1104 9.0 0.400 0.41 2.00 0.058
## 1105 7.5 0.705 0.24 1.80 0.360
## 1106 7.6 0.490 0.33 1.90 0.074
## 1107 8.7 0.420 0.45 2.40 0.072
## 1108 8.9 0.120 0.45 1.80 0.075
## 1109 7.8 0.600 0.26 2.00 0.080
## 1110 6.6 0.580 0.02 2.00 0.062
## 1111 9.1 0.300 0.41 2.00 0.068
## 1112 12.7 0.590 0.45 2.30 0.082
## 1113 8.7 0.675 0.10 1.60 0.090
## 1114 9.1 0.795 0.00 2.60 0.096
## 1115 11.5 0.590 0.59 2.60 0.087
## 1116 6.6 0.630 0.00 4.30 0.093
## 1117 6.7 0.420 0.27 8.60 0.068
## 1118 8.2 0.780 0.00 2.20 0.089
## 1119 5.3 0.470 0.11 2.20 0.048
## 1120 7.7 0.490 0.26 1.90 0.062
## 1121 9.0 0.450 0.49 2.60 0.084
## 1122 7.7 0.430 0.25 2.60 0.073
## 1123 6.7 0.700 0.08 3.75 0.067
## 1124 9.3 0.490 0.36 1.70 0.081
## 1125 8.9 0.320 0.31 2.00 0.088
## 1126 10.7 0.350 0.53 2.60 0.070
## 1127 8.3 0.780 0.10 2.60 0.081
## 1128 6.2 0.785 0.00 2.10 0.060
## 1129 5.8 0.680 0.02 1.80 0.087
## 1130 8.1 0.330 0.44 1.50 0.042
## 1131 7.5 0.270 0.34 2.30 0.050
## 1132 7.1 0.685 0.35 2.00 0.088
## 1133 6.1 0.380 0.15 1.80 0.072
## 1134 8.1 0.380 0.28 2.10 0.066
## 1135 6.6 0.800 0.03 7.80 0.079
## 1136 10.7 0.430 0.39 2.20 0.106
## 1137 11.5 0.350 0.49 3.30 0.070
## 1138 7.0 0.685 0.00 1.90 0.099
## 1139 8.9 0.595 0.41 7.90 0.086
## 1140 8.3 0.260 0.42 2.00 0.080
## 1141 7.7 0.260 0.30 1.70 0.059
## 1142 10.1 0.370 0.34 2.40 0.085
## 1143 7.2 0.660 0.03 2.30 0.078
## 1144 6.1 0.590 0.01 2.10 0.056
## 1145 9.7 0.320 0.54 2.50 0.094
## 1146 6.2 0.430 0.22 1.80 0.078
## 1147 7.6 0.420 0.08 2.70 0.084
## 1148 11.8 0.260 0.52 1.80 0.071
## 1149 9.7 0.660 0.34 2.60 0.094
## 1150 7.2 0.630 0.00 1.90 0.097
## 1151 8.9 0.480 0.24 2.85 0.094
## 1152 7.7 0.510 0.28 2.10 0.087
## 1153 10.6 0.340 0.49 3.20 0.078
## 1154 6.2 0.360 0.24 2.20 0.095
## 1155 6.9 0.570 0.00 2.80 0.081
## 1156 7.7 0.490 0.26 1.90 0.062
## 1157 7.2 0.520 0.07 1.40 0.074
## 1158 7.7 0.600 0.06 2.00 0.079
## 1159 7.1 0.310 0.30 2.20 0.053
## 1160 7.7 0.570 0.21 1.50 0.069
## 1161 9.0 0.820 0.14 2.60 0.089
## 1162 9.8 0.500 0.34 2.30 0.094
## 1163 12.7 0.600 0.49 2.80 0.075
## 1164 7.5 0.600 0.03 1.80 0.095
## 1165 6.4 0.380 0.14 2.20 0.038
## 1166 6.9 0.740 0.03 2.30 0.054
## 1167 9.2 0.530 0.24 2.60 0.078
## 1168 8.2 0.280 0.60 3.00 0.104
## 1169 7.6 1.580 0.00 2.10 0.137
## 1170 9.9 0.500 0.50 13.80 0.205
## 1171 7.6 0.790 0.21 2.30 0.087
## 1172 7.7 0.630 0.08 1.90 0.076
## 1173 8.7 0.540 0.26 2.50 0.097
## 1174 7.9 0.490 0.32 1.90 0.082
## 1175 6.9 0.630 0.02 1.90 0.078
## 1176 7.1 0.530 0.07 1.70 0.071
## 1177 9.9 0.350 0.41 2.30 0.083
## 1178 8.7 0.290 0.52 1.60 0.113
## 1179 7.7 0.705 0.10 2.60 0.084
## 1180 12.2 0.480 0.54 2.60 0.085
## 1181 6.6 0.725 0.09 5.50 0.117
## 1182 7.6 0.360 0.31 1.70 0.079
## 1183 6.2 0.520 0.08 4.40 0.071
## 1184 8.9 0.350 0.40 3.60 0.110
## 1185 7.2 0.620 0.06 2.70 0.077
## 1186 6.5 0.885 0.00 2.30 0.166
## 1187 5.6 0.310 0.78 13.90 0.074
## 1188 8.6 0.330 0.40 2.60 0.083
## 1189 8.5 0.460 0.31 2.25 0.078
## 1190 7.0 0.400 0.32 3.60 0.061
## 1191 10.3 0.590 0.42 2.80 0.090
## 1192 8.7 0.520 0.09 2.50 0.091
## 1193 6.6 0.735 0.02 7.90 0.122
## 1194 7.3 0.380 0.21 2.00 0.080
## 1195 5.0 0.380 0.01 1.60 0.048
## 1196 8.3 0.580 0.13 2.90 0.096
## 1197 10.3 0.430 0.44 2.40 0.214
## 1198 8.6 0.470 0.47 2.40 0.074
## 1199 5.0 1.020 0.04 1.40 0.045
## free sulfur dioxide total sulfur dioxide density pH sulphates alcohol
## 1 9.0 38.0 0.99616 3.15 0.53 9.80000
## 2 25.0 48.0 0.99810 3.14 0.56 9.70000
## 3 31.0 67.0 0.99668 3.26 0.46 9.20000
## 4 12.0 32.0 0.99735 3.36 0.61 10.00000
## 5 35.0 72.0 0.99700 3.44 0.52 9.40000
## 6 18.0 30.0 0.99402 3.45 0.54 11.20000
## 7 13.0 49.0 0.99880 3.18 0.65 11.00000
## 8 10.0 22.0 0.99828 3.39 0.68 10.60000
## 9 5.0 14.0 0.99476 3.21 1.03 11.60000
## 10 13.0 38.0 0.99980 3.28 0.59 9.70000
## 11 4.0 23.0 0.99960 3.28 0.97 10.10000
## 12 31.0 70.0 0.99685 3.42 0.59 9.50000
## 13 14.0 23.0 0.99760 3.35 0.61 11.30000
## 14 9.0 15.0 0.99514 3.35 0.58 11.00000
## 15 17.0 32.0 0.99686 3.15 0.67 10.60000
## 16 24.0 64.0 0.99350 3.38 0.57 11.70000
## 17 52.0 145.0 0.99860 3.16 0.88 9.20000
## 18 4.0 11.0 0.99608 3.39 0.52 10.00000
## 19 5.0 12.0 0.99580 3.54 0.60 11.20000
## 20 21.0 40.0 0.99680 3.43 0.63 9.70000
## 21 23.0 99.0 1.00289 3.22 0.68 9.30000
## 22 14.0 24.0 0.99428 3.45 0.87 11.20000
## 23 12.0 18.0 0.99568 3.32 0.56 10.50000
## 24 30.0 119.0 0.99700 3.20 0.56 9.40000
## 25 18.0 88.0 0.99157 3.68 0.73 13.60000
## 26 14.0 45.0 0.99800 3.19 0.73 10.00000
## 27 5.0 13.0 0.99800 3.28 0.83 11.50000
## 28 18.0 30.0 0.99712 3.40 0.75 9.80000
## 29 52.0 98.0 0.99736 3.28 0.50 9.50000
## 30 3.0 10.0 0.99698 3.12 0.59 9.50000
## 31 1.0 28.0 0.99900 3.41 0.87 10.30000
## 32 6.0 18.0 0.99720 3.29 0.61 9.20000
## 33 6.0 28.0 0.99704 3.07 0.65 10.03333
## 34 15.0 55.0 0.99680 3.46 0.59 10.20000
## 35 17.0 99.0 0.99370 3.63 0.63 13.00000
## 36 8.0 25.0 0.99720 3.47 0.67 9.50000
## 37 23.0 113.0 0.99733 3.13 0.48 9.20000
## 38 36.0 48.0 0.99520 3.20 0.58 9.80000
## 39 7.0 13.0 0.99658 3.30 0.62 12.10000
## 40 6.0 15.0 0.99880 2.94 0.66 9.20000
## 41 25.0 45.0 0.99780 3.34 0.53 9.50000
## 42 10.0 20.0 0.99730 3.34 0.79 12.20000
## 43 33.0 76.0 0.99878 3.14 0.55 9.40000
## 44 21.0 31.0 0.99629 3.45 0.63 10.30000
## 45 21.0 73.0 0.99700 3.36 0.57 9.10000
## 46 17.0 106.0 0.99320 3.85 0.60 12.90000
## 47 45.0 88.0 0.99524 3.33 0.76 11.80000
## 48 5.0 18.0 0.99666 3.20 0.52 9.40000
## 49 5.0 11.0 0.99840 3.37 0.69 10.10000
## 50 16.0 24.0 0.99376 3.56 0.77 11.10000
## 51 7.0 12.0 0.99354 3.25 0.55 12.30000
## 52 10.0 22.0 0.99760 3.22 0.44 9.60000
## 53 8.0 17.0 0.99774 3.32 0.71 10.50000
## 54 16.0 29.0 0.99620 3.21 0.49 10.20000
## 55 14.0 28.0 0.99516 3.18 0.80 11.20000
## 56 6.0 18.0 0.99620 3.20 1.13 12.00000
## 57 11.0 28.0 0.99940 3.36 0.86 9.40000
## 58 20.0 47.0 0.99880 3.26 0.67 9.60000
## 59 5.0 16.0 0.99940 3.16 0.63 8.40000
## 60 12.0 49.0 0.99625 3.37 0.52 9.90000
## 61 27.0 55.0 0.99620 3.31 0.63 11.00000
## 62 6.0 19.0 0.99860 3.12 0.82 9.30000
## 63 28.0 89.0 0.99840 3.22 0.68 10.00000
## 64 6.0 14.0 0.99555 3.51 0.69 11.00000
## 65 6.0 18.0 0.99620 3.20 1.13 12.00000
## 66 25.0 60.0 0.99638 3.29 0.75 10.90000
## 67 6.0 20.0 0.99772 3.15 0.50 10.50000
## 68 21.0 119.0 0.99800 3.09 0.52 9.30000
## 69 3.0 8.0 0.99642 3.20 0.53 11.90000
## 70 16.0 37.0 0.99720 3.31 0.58 10.70000
## 71 3.0 12.0 0.99745 3.31 0.65 9.55000
## 72 5.0 13.0 0.99420 3.72 0.58 11.40000
## 73 5.0 18.0 0.99860 3.40 0.55 9.60000
## 74 11.0 41.0 0.99439 3.52 0.80 12.40000
## 75 5.0 16.0 0.99358 3.15 0.81 12.50000
## 76 15.0 105.0 0.99600 3.27 0.54 9.40000
## 77 30.0 45.0 0.99590 3.26 0.58 10.00000
## 78 14.0 25.0 0.99388 3.35 0.63 12.00000
## 79 27.0 60.0 0.99630 3.28 0.59 9.80000
## 80 32.0 151.0 0.99800 3.15 0.53 9.50000
## 81 12.0 19.0 0.99434 3.14 0.71 11.80000
## 82 36.0 51.0 0.99836 3.38 0.86 9.90000
## 83 11.0 18.0 0.99620 3.41 0.59 10.80000
## 84 7.0 28.0 0.99700 3.37 0.50 9.40000
## 85 18.0 82.0 0.99708 3.33 0.68 9.70000
## 86 7.0 14.0 0.99517 3.40 0.58 10.90000
## 87 17.0 40.0 0.99803 3.29 0.55 9.50000
## 88 17.0 46.0 0.99760 3.26 0.51 9.30000
## 89 5.0 28.0 0.99880 3.14 0.60 10.20000
## 90 10.0 42.0 0.99690 3.19 0.59 9.50000
## 91 22.0 54.0 0.99960 3.15 0.89 9.90000
## 92 14.0 33.0 0.99965 3.36 0.80 10.50000
## 93 6.0 18.0 0.99720 3.29 0.61 9.20000
## 94 8.0 25.0 0.99746 3.69 0.73 10.50000
## 95 16.0 37.0 0.99850 3.22 0.78 10.00000
## 96 5.0 10.0 0.99419 3.27 0.54 11.20000
## 97 35.0 103.0 0.99690 3.30 0.75 10.50000
## 98 12.0 57.0 0.99780 3.30 0.47 9.00000
## 99 39.0 145.0 0.99750 3.04 1.03 9.30000
## 100 13.0 27.0 0.99516 3.26 0.84 11.70000
## 101 5.0 9.0 0.99632 3.38 0.55 10.90000
## 102 3.0 9.0 0.99901 3.18 0.60 10.90000
## 103 25.0 49.0 0.99970 3.10 0.76 10.30000
## 104 36.0 109.0 0.99007 2.89 0.44 12.70000
## 105 14.0 104.0 0.99760 3.28 0.62 9.40000
## 106 8.0 19.0 0.99620 3.34 0.95 10.50000
## 107 28.0 90.0 0.99784 3.15 0.57 9.10000
## 108 11.0 63.0 0.99652 3.29 0.48 9.80000
## 109 26.0 62.0 0.99940 3.18 0.63 10.20000
## 110 6.0 25.0 0.99970 3.03 0.57 9.90000
## 111 11.0 37.0 0.99790 3.43 0.76 9.70000
## 112 29.0 55.0 1.00010 3.26 0.88 11.00000
## 113 27.0 90.0 0.99914 3.15 0.65 8.50000
## 114 10.0 89.0 0.99620 3.30 0.57 9.00000
## 115 17.0 42.0 0.99720 3.31 0.54 9.60000
## 116 40.0 64.0 0.99529 3.12 0.49 9.60000
## 117 7.0 11.0 0.99331 3.29 0.52 11.50000
## 118 6.0 24.0 0.99880 3.11 0.99 13.30000
## 119 6.0 15.0 0.99524 3.30 0.74 11.80000
## 120 13.0 50.0 1.00150 3.16 0.69 9.20000
## 121 13.0 26.0 0.99547 3.38 0.64 9.80000
## 122 15.0 38.0 0.99634 3.01 1.10 9.40000
## 123 10.0 19.0 0.99660 3.39 0.47 9.60000
## 124 12.0 78.0 0.99640 3.39 0.71 11.00000
## 125 26.0 48.0 0.99661 3.47 0.77 9.70000
## 126 8.0 15.0 0.99498 3.35 0.62 10.40000
## 127 17.0 31.0 0.99640 3.33 0.56 10.00000
## 128 4.0 8.0 0.99720 3.36 0.33 9.10000
## 129 6.0 12.0 0.99408 3.26 0.62 12.40000
## 130 10.0 79.0 0.99677 3.29 0.69 9.50000
## 131 11.0 23.0 0.99550 3.34 0.56 9.30000
## 132 16.0 34.0 0.99720 3.47 0.70 9.90000
## 133 23.0 143.0 0.99780 3.28 0.55 9.40000
## 134 29.0 58.0 0.99740 3.31 0.64 10.30000
## 135 15.0 64.0 0.99588 3.22 0.59 9.50000
## 136 11.0 44.0 0.99888 3.31 0.55 9.50000
## 137 18.0 69.0 0.99730 3.08 1.31 9.30000
## 138 31.0 131.0 0.99622 3.21 0.52 9.90000
## 139 7.0 15.0 0.99730 3.22 0.64 10.10000
## 140 15.0 39.0 0.99577 3.53 0.54 11.10000
## 141 5.0 14.0 1.00020 3.19 0.44 9.60000
## 142 20.0 27.0 0.99546 3.15 0.69 11.20000
## 143 17.0 102.0 0.99780 3.35 0.80 10.50000
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## 178 21.0 50.0 0.99820 3.37 0.91 9.90000
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## 181 5.0 22.0 0.99980 3.26 0.74 11.20000
## 182 6.0 31.0 0.99840 3.19 0.70 10.10000
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## 193 15.0 31.0 0.99660 3.15 0.57 11.00000
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## 211 9.0 39.0 0.99870 3.48 0.62 9.30000
## 212 16.0 34.0 0.99720 3.47 0.70 9.90000
## 213 39.0 106.0 0.99660 3.36 0.78 11.00000
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## 221 14.0 36.0 0.99614 3.23 0.49 9.30000
## 222 7.0 20.0 0.99960 3.24 0.76 10.40000
## 223 5.0 15.0 0.99880 3.36 0.49 9.10000
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## 227 11.0 34.0 0.99780 3.51 0.56 9.40000
## 228 6.0 33.0 0.99740 3.09 0.57 9.40000
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## 230 29.0 66.0 0.99710 3.45 0.59 9.50000
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## 232 16.0 49.0 0.99545 3.36 0.79 9.50000
## 233 23.0 43.0 0.99860 3.04 0.68 11.40000
## 234 21.0 114.0 0.99730 3.22 0.54 9.40000
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## 236 6.0 18.0 0.99790 3.30 0.71 10.50000
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## 241 32.0 44.0 0.99490 3.45 0.58 10.50000
## 242 18.0 47.0 0.99560 3.37 0.62 10.40000
## 243 15.0 105.0 0.99620 3.33 0.54 9.50000
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## 245 17.0 99.0 0.99370 3.63 0.63 13.00000
## 246 6.0 11.0 0.99585 3.23 0.52 12.00000
## 247 5.0 31.0 0.99930 3.67 0.80 9.70000
## 248 15.0 37.0 0.99730 3.35 0.86 12.80000
## 249 11.0 31.0 0.99615 3.33 0.86 12.00000
## 250 23.0 44.0 0.99656 3.38 0.79 11.10000
## 251 12.0 49.0 0.99680 3.21 0.57 10.00000
## 252 20.0 85.0 0.99746 3.55 0.59 9.80000
## 253 5.0 35.0 0.99738 3.25 0.42 9.60000
## 254 5.0 15.0 1.00000 3.19 0.43 9.20000
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## 256 3.0 10.0 0.99639 3.27 0.64 12.20000
## 257 42.0 74.0 0.99451 2.98 0.63 11.80000
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## 260 5.0 11.0 0.99580 3.33 0.53 10.30000
## 261 3.0 13.0 0.99800 3.49 0.52 9.20000
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## 263 19.0 38.0 0.99900 3.35 0.60 9.30000
## 264 22.0 46.0 0.99880 3.32 0.67 9.70000
## 265 32.0 44.0 0.99547 3.57 0.71 10.20000
## 266 28.0 104.0 0.99830 3.10 0.56 9.20000
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## 269 7.0 34.0 0.99788 3.36 0.61 10.50000
## 270 4.0 11.0 0.99700 3.46 0.68 9.50000
## 271 6.0 32.0 0.99880 3.26 0.56 10.60000
## 272 14.0 31.0 0.99545 3.36 0.58 10.40000
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## 277 5.0 20.0 0.99730 3.32 0.81 9.60000
## 278 14.0 24.0 0.99252 3.30 0.53 13.30000
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## 280 14.0 31.0 0.99680 3.50 0.67 11.00000
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## 282 5.0 20.0 0.99660 3.12 0.59 9.90000
## 283 6.0 18.0 1.00040 3.16 0.49 9.50000
## 284 12.0 69.0 0.99633 3.32 0.70 11.00000
## 285 3.0 11.0 0.99378 3.71 0.63 12.80000
## 286 4.0 23.0 0.99960 3.28 0.97 10.10000
## 287 3.0 15.0 0.99940 3.20 0.78 9.60000
## 288 10.0 28.0 0.99640 3.33 0.67 11.20000
## 289 26.0 88.0 0.99840 3.08 0.57 10.10000
## 290 13.0 33.0 0.99920 3.36 0.45 9.40000
## 291 16.0 29.0 0.99588 3.30 0.78 10.80000
## 292 6.0 16.0 0.99700 3.19 0.70 11.30000
## 293 8.0 38.0 0.99720 3.58 0.61 10.50000
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## 295 18.0 30.0 0.99400 3.29 0.69 11.20000
## 296 13.0 38.0 0.99748 3.48 0.65 9.80000
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## 303 30.0 134.0 0.99680 3.24 0.66 9.40000
## 304 34.0 102.0 0.99414 3.27 0.78 12.80000
## 305 13.0 100.0 0.99586 3.26 0.52 9.90000
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## 307 15.0 26.0 0.99448 3.36 0.45 9.50000
## 308 24.0 69.0 0.99556 3.40 0.53 10.10000
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## 310 10.0 37.0 0.99685 3.46 0.50 11.00000
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## 312 11.0 34.0 0.99780 3.51 0.56 9.40000
## 313 20.0 49.0 0.99760 3.34 0.86 10.60000
## 314 16.0 61.0 0.99572 3.29 0.60 9.30000
## 315 24.0 64.0 0.99746 3.10 0.74 9.60000
## 316 10.0 21.0 0.99552 3.41 0.76 11.90000
## 317 21.0 39.0 0.99692 3.33 0.83 11.10000
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## 319 38.0 142.0 0.99780 3.22 0.55 9.40000
## 320 12.0 32.0 0.99689 3.58 0.66 9.80000
## 321 26.0 51.0 0.99456 3.38 0.72 11.80000
## 322 52.0 73.0 0.99786 3.47 0.90 10.20000
## 323 37.0 92.0 0.99960 3.30 0.65 10.10000
## 324 5.0 13.0 0.99760 3.05 0.74 10.50000
## 325 6.0 12.0 0.99551 3.56 0.51 10.80000
## 326 10.0 23.0 1.00315 2.92 0.74 11.10000
## 327 12.0 22.0 0.99690 3.48 0.50 9.30000
## 328 18.0 34.0 0.99480 3.39 0.60 10.60000
## 329 17.0 104.0 0.99584 3.28 0.52 9.90000
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## 331 15.0 26.0 0.99448 3.36 0.45 9.50000
## 332 18.0 95.0 0.99660 3.22 0.67 9.40000
## 333 32.0 55.0 0.99880 3.34 0.75 8.70000
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## 335 6.0 18.0 0.99860 3.04 1.05 9.40000
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## 342 9.0 42.0 0.99630 3.10 0.55 9.40000
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## 346 33.0 141.0 0.99620 3.25 0.51 9.90000
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## 350 18.0 77.0 0.99922 3.15 0.51 9.40000
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## 355 14.0 39.0 0.99522 3.34 0.53 10.80000
## 356 6.0 24.0 0.99840 3.31 0.59 9.20000
## 357 12.0 42.0 1.00040 3.16 0.61 10.30000
## 358 34.0 52.0 0.99760 3.62 0.68 9.90000
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## 360 33.0 73.0 0.99550 3.17 0.63 10.20000
## 361 9.0 14.0 0.99488 3.42 0.55 11.50000
## 362 12.0 89.0 0.99630 3.04 0.90 10.10000
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## 367 43.0 113.0 0.99660 3.32 0.79 11.10000
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## 379 11.0 19.0 0.99630 3.23 0.63 11.60000
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## 382 12.0 31.0 0.99790 3.40 0.48 9.90000
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## 384 6.0 26.0 0.99791 3.07 0.60 10.40000
## 385 19.0 27.0 0.99545 3.28 0.73 11.90000
## 386 11.0 65.0 1.00260 3.32 0.64 10.40000
## 387 25.0 52.0 0.99700 3.20 0.71 11.40000
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## 390 6.0 21.0 1.00000 3.17 0.62 9.20000
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## 392 19.0 58.0 1.00020 3.50 0.65 9.30000
## 393 24.0 125.0 0.99840 3.31 0.61 9.40000
## 394 12.0 20.0 0.99546 3.29 0.54 10.10000
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## 416 14.0 57.0 0.99690 3.37 0.46 10.30000
## 417 38.0 106.0 0.99820 3.08 0.59 9.10000
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## 419 27.0 33.0 0.99220 3.45 0.48 12.30000
## 420 5.0 14.0 1.00100 3.25 0.74 11.90000
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## 423 34.0 66.0 0.99726 3.15 0.58 9.80000
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## 426 5.0 30.0 0.99670 3.20 0.48 9.80000
## 427 29.0 44.0 0.99489 3.38 0.83 10.30000
## 428 5.0 15.0 0.99910 3.32 0.60 9.00000
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## 430 27.0 66.0 0.99820 3.17 0.76 10.80000
## 431 21.0 122.0 0.99840 3.32 0.62 9.40000
## 432 3.0 8.0 0.99600 3.41 0.47 10.30000
## 433 29.0 40.0 0.99574 3.42 0.75 11.00000
## 434 13.0 49.0 0.99620 3.14 0.57 11.00000
## 435 4.0 10.0 0.99554 3.12 0.48 9.10000
## 436 15.0 26.0 0.99448 3.36 0.45 9.50000
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## 438 8.0 23.0 0.99740 3.26 0.70 9.60000
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## 441 24.0 122.0 0.99820 3.26 0.61 9.50000
## 442 11.0 18.0 0.99620 3.41 0.59 10.80000
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## 444 6.0 23.0 0.99830 3.61 0.96 9.90000
## 445 11.0 34.0 0.99760 3.44 0.53 9.50000
## 446 10.0 26.0 0.99840 3.34 0.75 10.20000
## 447 6.0 17.0 0.99640 3.15 0.92 11.70000
## 448 31.0 68.0 0.99694 3.45 0.48 9.40000
## 449 7.0 27.0 0.99880 3.06 0.68 11.30000
## 450 35.0 121.0 0.99810 3.37 0.49 9.40000
## 451 24.0 58.0 0.99650 3.34 0.58 9.40000
## 452 11.0 54.0 0.99900 3.37 0.49 9.90000
## 453 10.0 29.0 0.99620 3.26 0.74 9.80000
## 454 10.0 62.0 0.99685 3.35 0.56 10.00000
## 455 12.0 28.0 0.99768 3.52 0.73 9.50000
## 456 3.0 6.0 0.99690 3.13 0.63 10.40000
## 457 33.0 79.0 0.99690 3.41 0.65 9.50000
## 458 12.0 56.0 0.99680 3.49 0.67 9.20000
## 459 20.0 110.0 0.99914 3.30 1.17 10.20000
## 460 19.0 58.0 0.99910 3.18 0.56 10.10000
## 461 15.0 39.0 0.99675 3.37 0.58 9.20000
## 462 29.0 44.0 0.99489 3.38 0.83 10.30000
## 463 13.0 29.0 0.99397 3.42 0.62 11.70000
## 464 5.5 48.0 0.99612 3.40 0.60 10.30000
## 465 7.0 23.0 0.99620 3.26 0.47 10.00000
## 466 21.0 59.0 0.99714 3.31 0.67 10.10000
## 467 43.0 60.0 0.99499 3.10 0.42 9.20000
## 468 9.0 22.0 0.99619 3.33 0.68 10.90000
## 469 27.0 43.0 0.99550 3.42 0.58 10.70000
## 470 12.0 80.0 0.99580 3.38 0.52 10.10000
## 471 16.0 33.0 0.99736 3.58 0.69 10.80000
## 472 10.0 28.0 0.99609 3.29 0.51 9.90000
## 473 6.0 33.0 0.99740 3.09 0.57 9.40000
## 474 16.0 56.0 0.99680 3.11 1.28 9.30000
## 475 30.0 47.0 0.99554 3.36 0.48 9.40000
## 476 35.0 63.0 0.99750 3.57 0.78 9.90000
## 477 21.0 68.0 0.99920 3.46 0.63 9.50000
## 478 27.0 94.0 0.99915 3.38 0.77 9.40000
## 479 36.0 121.0 0.99820 3.37 0.49 9.40000
## 480 12.0 25.0 0.99516 3.26 0.84 11.70000
## 481 15.0 113.0 0.99660 3.17 0.66 9.80000
## 482 7.0 27.0 0.99870 3.69 0.91 9.40000
## 483 5.0 10.0 0.99670 3.39 0.56 9.80000
## 484 22.0 37.0 0.99660 3.46 0.57 10.60000
## 485 25.0 60.0 0.99638 3.29 0.75 10.90000
## 486 31.0 54.0 0.99728 3.30 0.65 10.00000
## 487 17.0 91.0 0.99616 3.29 0.56 9.80000
## 488 7.0 37.0 0.99685 3.32 0.55 9.00000
## 489 9.0 17.0 0.99467 3.50 0.63 10.90000
## 490 11.0 25.0 0.99670 3.32 0.87 8.70000
## 491 21.0 39.0 0.99692 3.33 0.83 11.10000
## 492 6.0 24.0 0.99700 3.06 0.66 13.40000
## 493 10.0 41.0 0.99638 3.18 0.69 11.90000
## 494 31.0 70.0 0.99685 3.42 0.59 9.50000
## 495 5.0 15.0 0.99700 3.37 0.56 11.30000
## 496 6.0 11.0 0.99538 3.44 0.54 12.20000
## 497 8.0 20.0 0.99920 3.07 0.82 10.30000
## 498 16.0 72.0 0.99570 3.15 0.57 9.40000
## 499 17.0 53.0 1.00140 3.05 0.81 9.50000
## 500 24.0 33.0 0.99540 3.27 0.55 9.70000
## 501 5.0 16.0 1.00060 2.98 0.54 9.40000
## 502 8.0 38.0 0.99800 3.47 0.66 9.60000
## 503 23.0 94.0 0.99686 3.21 0.58 9.50000
## 504 6.0 22.0 0.99760 3.21 1.05 10.80000
## 505 48.0 82.0 1.00242 3.16 0.75 8.80000
## 506 5.0 27.0 1.00010 3.05 0.64 9.50000
## 507 10.0 41.0 0.99800 3.23 0.66 12.50000
## 508 6.0 25.0 0.99980 3.12 0.59 9.30000
## 509 24.0 94.0 0.99744 3.55 0.53 9.70000
## 510 24.0 48.0 0.99554 3.34 0.86 11.00000
## 511 22.0 53.0 0.99570 3.32 0.62 9.30000
## 512 4.0 14.0 0.99940 3.44 0.52 11.50000
## 513 17.0 43.0 0.99822 3.33 0.60 10.40000
## 514 7.0 49.0 0.99718 3.35 0.68 10.30000
## 515 4.0 8.0 0.99280 3.36 0.55 12.80000
## 516 12.0 31.0 0.99840 3.26 0.65 10.20000
## 517 10.0 28.0 0.99613 3.25 0.53 10.20000
## 518 8.0 24.0 0.99780 3.48 0.53 9.00000
## 519 3.0 11.0 0.99440 3.48 0.85 11.70000
## 520 30.0 69.0 0.99787 3.22 0.50 10.00000
## 521 15.0 28.0 0.99470 3.23 0.73 11.30000
## 522 20.0 65.0 0.99558 3.40 0.54 10.10000
## 523 12.0 30.0 0.99960 3.18 0.63 10.80000
## 524 16.0 49.0 0.99820 3.19 0.70 10.00000
## 525 10.0 19.0 0.99560 3.40 0.47 10.00000
## 526 14.0 28.0 0.99362 3.62 0.67 12.40000
## 527 40.0 83.0 0.99930 3.45 0.52 9.40000
## 528 8.0 17.0 0.99774 3.32 0.71 10.50000
## 529 3.0 16.0 0.99620 3.42 0.92 10.50000
## 530 5.0 13.0 0.99760 3.29 0.55 10.40000
## 531 15.0 88.0 0.99357 3.66 0.60 11.50000
## 532 5.0 17.0 0.99880 3.21 0.73 11.00000
## 533 3.0 6.0 0.99512 3.27 0.67 11.90000
## 534 11.0 35.0 0.99557 3.36 0.62 10.80000
## 535 30.0 74.0 0.99865 3.30 0.64 10.40000
## 536 22.0 91.0 0.99790 3.29 0.62 10.10000
## 537 15.0 24.0 0.99514 3.44 0.68 10.55000
## 538 14.0 31.0 0.99689 3.59 0.66 9.80000
## 539 22.0 31.0 0.99635 3.38 0.58 10.00000
## 540 6.0 39.0 0.99346 3.33 0.46 11.90000
## 541 9.0 17.0 0.99484 3.19 0.52 12.50000
## 542 15.0 34.0 0.99655 3.49 0.68 10.50000
## 543 27.0 65.0 0.99690 3.28 0.79 10.70000
## 544 28.0 65.0 0.99590 3.30 0.43 10.10000
## 545 15.0 33.0 0.99538 3.44 0.63 11.30000
## 546 7.0 24.0 1.00012 3.09 0.68 10.90000
## 547 15.0 50.0 0.99467 3.58 0.67 12.50000
## 548 11.0 37.0 0.99728 3.16 0.67 10.80000
## 549 5.0 77.0 0.99747 3.13 0.62 9.10000
## 550 3.0 7.0 0.99286 3.22 0.37 13.00000
## 551 12.0 16.0 0.99650 3.45 0.68 11.50000
## 552 4.0 28.0 0.99464 3.36 0.44 10.10000
## 553 5.0 62.0 0.99710 3.00 1.09 9.30000
## 554 8.0 28.0 0.99815 3.28 0.60 10.00000
## 555 17.0 119.0 0.99675 3.26 0.57 9.30000
## 556 24.0 44.0 0.99534 3.40 0.85 11.00000
## 557 13.0 54.0 0.99724 3.36 0.54 10.10000
## 558 18.0 27.0 0.99768 3.44 0.54 9.40000
## 559 8.0 14.0 0.99680 3.21 0.90 10.50000
## 560 4.0 11.0 0.99620 3.28 0.59 9.50000
## 561 21.0 101.0 1.00010 3.13 0.51 9.50000
## 562 6.0 9.0 0.99628 3.10 0.48 10.40000
## 563 31.0 145.0 0.99780 3.24 0.53 9.40000
## 564 7.0 60.0 0.99786 2.99 1.18 10.20000
## 565 24.0 60.0 0.99790 3.31 0.70 10.80000
## 566 9.0 42.0 0.99860 3.54 0.66 10.50000
## 567 6.0 10.0 0.99604 3.13 0.58 11.90000
## 568 15.0 47.0 0.99960 3.05 0.61 10.60000
## 569 12.0 21.0 0.99551 3.21 0.75 10.20000
## 570 6.0 14.0 0.99820 3.30 0.75 9.80000
## 571 4.0 14.0 0.99800 3.29 0.54 9.70000
## 572 7.0 16.0 0.99508 3.45 0.63 11.50000
## 573 6.0 14.0 0.99627 3.45 0.58 9.80000
## 574 26.0 96.0 0.99814 3.36 0.53 10.00000
## 575 72.0 160.0 0.99721 3.33 0.54 10.30000
## 576 16.0 36.0 0.99564 3.38 0.60 10.30000
## 577 13.0 28.0 0.99631 3.60 0.66 10.20000
## 578 29.0 63.0 0.99615 3.37 0.58 10.50000
## 579 53.0 77.0 0.99604 3.47 0.87 11.00000
## 580 15.0 40.0 1.00040 3.06 0.49 9.00000
## 581 9.0 21.0 0.99526 3.39 0.66 11.60000
## 582 23.0 116.0 0.99760 3.23 0.64 9.50000
## 583 5.0 20.0 0.99900 3.31 0.79 10.70000
## 584 6.0 24.0 0.99630 3.44 0.82 11.90000
## 585 5.0 13.0 0.99800 3.22 0.62 9.20000
## 586 12.0 93.0 0.99980 3.48 0.54 9.80000
## 587 13.0 34.0 0.99670 3.29 0.48 9.20000
## 588 6.0 25.0 0.99581 3.09 0.43 9.70000
## 589 23.0 147.0 0.99636 3.26 0.59 9.70000
## 590 20.0 57.0 0.99598 3.60 0.74 11.70000
## 591 39.0 51.0 0.99512 3.52 0.76 11.20000
## 592 14.0 21.0 0.99556 3.14 0.51 10.90000
## 593 35.0 53.0 0.99500 3.27 1.01 12.40000
## 594 19.0 27.0 0.99712 3.44 0.67 9.80000
## 595 26.0 35.0 0.99356 3.22 0.59 12.50000
## 596 18.0 45.0 0.99625 3.29 0.65 9.30000
## 597 16.0 40.0 0.99910 3.39 0.62 9.40000
## 598 10.0 39.0 0.99562 3.40 0.69 11.80000
## 599 18.0 80.0 0.99760 3.37 0.80 9.60000
## 600 26.0 42.0 0.99590 3.18 0.83 10.60000
## 601 12.0 26.0 0.99306 3.55 0.51 12.10000
## 602 12.0 67.0 0.99565 3.35 0.60 9.40000
## 603 16.0 126.0 0.99690 3.28 0.61 9.50000
## 604 11.0 43.0 0.99760 3.31 0.53 9.20000
## 605 16.0 42.0 0.99720 3.44 0.52 9.30000
## 606 30.0 35.0 0.99533 3.48 0.65 11.40000
## 607 15.0 54.0 1.00000 3.03 0.66 10.20000
## 608 11.0 38.0 0.99699 3.24 0.56 10.90000
## 609 12.0 42.0 1.00040 3.16 0.61 10.30000
## 610 10.0 26.0 1.00060 3.48 0.91 9.70000
## 611 27.0 63.0 0.99160 3.68 0.79 14.00000
## 612 5.0 14.0 0.99710 3.26 0.79 10.60000
## 613 9.0 25.0 0.99580 3.33 0.56 9.50000
## 614 23.0 48.0 0.99810 3.41 0.74 9.20000
## 615 10.0 14.0 0.99538 3.51 0.71 11.70000
## 616 34.0 45.0 0.99417 3.46 0.74 12.70000
## 617 21.0 28.0 0.99352 3.44 0.55 12.30000
## 618 35.0 67.0 0.99720 3.28 0.73 11.80000
## 619 12.0 28.0 0.99940 3.51 0.72 10.00000
## 620 5.0 19.0 0.99585 3.30 0.86 12.40000
## 621 16.0 42.0 0.99154 3.71 0.74 14.00000
## 622 10.0 35.0 0.99575 3.34 0.57 10.00000
## 623 24.0 144.0 0.99655 3.30 0.60 9.50000
## 624 51.0 148.0 0.99860 3.17 0.93 9.20000
## 625 12.0 66.0 0.99623 3.00 1.17 9.20000
## 626 7.0 17.0 0.99659 3.26 0.64 9.40000
## 627 18.0 88.0 0.99745 3.53 0.55 9.50000
## 628 18.0 44.0 0.99210 3.90 0.62 12.80000
## 629 12.0 47.0 0.99640 3.19 0.93 9.50000
## 630 16.0 28.0 0.99531 3.36 0.64 10.10000
## 631 6.0 15.0 1.00080 2.86 0.79 8.40000
## 632 13.0 62.0 0.99660 3.07 0.63 10.50000
## 633 14.0 46.0 0.99710 3.24 0.66 9.60000
## 634 4.0 13.0 0.99855 3.36 0.49 9.50000
## 635 21.0 127.0 0.99760 3.23 0.62 9.40000
## 636 27.0 61.0 0.99650 3.36 0.67 10.70000
## 637 6.0 26.0 0.99800 3.18 0.51 9.50000
## 638 3.0 10.0 0.99586 3.21 0.59 12.10000
## 639 24.0 45.0 0.99528 3.60 0.68 10.30000
## 640 16.0 62.0 0.99790 3.03 1.17 9.00000
## 641 16.0 46.0 0.99258 4.01 0.59 12.50000
## 642 8.0 15.0 0.99560 3.40 0.63 9.40000
## 643 25.0 67.0 0.99680 3.20 0.68 9.80000
## 644 6.0 10.0 0.99664 3.12 0.65 11.80000
## 645 15.0 49.0 0.99800 3.03 0.81 9.70000
## 646 6.0 12.0 0.99495 3.44 0.72 11.50000
## 647 22.0 54.0 0.99572 3.42 1.16 10.60000
## 648 7.0 22.0 0.99669 3.46 0.61 10.20000
## 649 16.0 70.0 0.99352 3.46 0.65 12.50000
## 650 18.0 38.0 0.99330 3.38 0.88 13.60000
## 651 6.0 21.0 0.99870 3.26 0.86 10.70000
## 652 5.0 35.0 0.99738 3.25 0.42 9.60000
## 653 22.0 84.0 0.99720 3.32 0.70 9.60000
## 654 5.0 13.0 0.99680 3.20 0.52 9.50000
## 655 17.0 91.0 0.99616 3.26 0.58 9.80000
## 656 9.0 46.0 0.99620 3.41 0.54 9.40000
## 657 20.0 53.0 1.00040 3.14 0.61 9.40000
## 658 6.0 24.0 0.99780 3.15 0.90 11.00000
## 659 6.0 17.0 0.99565 3.22 0.63 11.80000
## 660 48.0 59.0 0.99541 3.61 0.70 11.50000
## 661 13.0 24.0 0.99438 3.41 0.83 11.30000
## 662 21.0 58.0 0.99970 3.46 0.72 10.20000
## 663 19.0 48.0 0.99490 3.49 0.49 11.40000
## 664 27.0 63.0 0.99160 3.68 0.79 14.00000
## 665 3.0 8.0 0.99817 3.27 0.58 11.00000
## 666 10.0 19.0 0.99468 3.30 0.73 12.00000
## 667 23.0 49.0 0.99630 3.28 0.67 9.30000
## 668 26.0 42.0 0.99489 3.39 0.82 10.90000
## 669 21.0 92.0 0.99745 3.50 0.60 9.80000
## 670 18.0 72.0 1.00010 3.31 0.53 9.70000
## 671 15.0 23.0 0.99627 3.54 0.60 11.00000
## 672 14.0 38.0 0.99675 3.37 0.58 9.00000
## 673 25.0 105.0 0.99613 3.30 0.49 9.90000
## 674 17.0 35.0 0.99940 3.10 0.61 10.80000
## 675 8.0 23.0 0.99610 3.30 0.58 9.60000
## 676 26.0 70.0 0.99810 3.16 0.52 9.90000
## 677 22.0 36.0 0.99490 3.41 0.75 11.10000
## 678 6.0 20.0 0.99652 3.28 0.57 12.50000
## 679 6.0 21.0 1.00020 3.16 0.67 9.70000
## 680 3.0 19.0 0.99500 3.16 0.46 9.80000
## 681 10.0 48.0 0.99760 3.34 0.46 9.40000
## 682 8.0 30.0 0.99840 3.23 0.58 10.50000
## 683 10.0 37.0 0.99685 3.46 0.50 11.00000
## 684 12.0 47.0 0.99640 3.19 0.93 9.50000
## 685 4.0 10.0 0.99386 3.27 0.71 12.50000
## 686 28.0 128.0 0.99730 3.42 0.71 10.50000
## 687 68.0 124.0 0.99940 3.47 0.53 9.90000
## 688 12.0 25.0 0.99520 3.34 0.79 13.30000
## 689 7.0 17.0 0.99840 3.08 0.67 9.30000
## 690 22.0 67.0 0.99740 3.13 1.28 9.40000
## 691 9.0 28.0 0.99940 3.20 0.77 10.80000
## 692 24.0 88.0 0.99612 3.30 0.53 9.80000
## 693 5.0 11.0 0.99620 3.48 0.52 9.50000
## 694 23.0 58.0 0.99633 3.42 0.97 10.60000
## 695 15.0 58.0 0.99470 3.37 0.78 11.80000
## 696 33.0 98.0 1.00100 3.20 0.95 9.20000
## 697 24.0 32.0 0.99402 3.54 0.60 11.30000
## 698 9.0 105.0 0.99725 3.25 1.18 10.50000
## 699 32.0 64.0 0.99491 3.43 0.56 11.20000
## 700 12.0 20.0 0.99546 3.29 0.54 10.10000
## 701 22.0 47.0 0.99525 3.51 0.43 10.70000
## 702 8.0 22.0 0.99322 3.38 0.49 11.70000
## 703 13.0 20.0 0.99540 3.42 0.67 11.30000
## 704 12.0 48.0 0.99760 3.18 0.51 9.60000
## 705 27.0 73.0 0.99669 3.32 0.48 9.20000
## 706 12.0 88.0 0.99240 3.56 0.82 12.90000
## 707 6.0 18.0 0.99610 3.40 0.59 10.30000
## 708 19.0 27.0 0.99596 3.16 0.49 9.40000
## 709 55.0 95.0 1.00369 3.18 0.77 9.00000
## 710 5.0 15.0 0.99800 3.21 0.67 10.20000
## 711 7.0 37.0 0.99685 3.32 0.55 9.00000
## 712 6.0 19.0 0.99820 3.26 0.61 9.30000
## 713 13.0 27.0 0.99516 3.26 0.84 11.70000
## 714 7.0 20.0 0.99410 3.28 0.70 11.10000
## 715 11.0 18.0 0.99242 3.39 0.40 12.80000
## 716 14.0 111.0 0.99640 3.15 0.66 9.80000
## 717 9.0 17.0 0.99496 3.52 0.78 10.60000
## 718 8.0 16.0 0.99640 3.38 0.59 9.80000
## 719 17.0 31.0 0.99328 3.35 0.54 12.40000
## 720 34.0 61.0 0.99528 3.43 0.59 10.80000
## 721 3.0 9.0 0.99506 3.27 0.55 12.30000
## 722 17.0 91.0 0.99616 3.29 0.56 9.80000
## 723 9.0 23.0 0.99585 3.39 0.43 9.05000
## 724 9.0 34.0 0.99736 3.24 0.77 12.10000
## 725 17.0 54.0 0.99750 3.43 0.59 10.00000
## 726 10.0 24.0 0.99820 3.20 0.89 9.40000
## 727 6.0 12.0 0.99672 3.34 0.49 9.70000
## 728 6.0 18.0 1.00040 3.16 0.49 9.50000
## 729 13.0 38.0 0.99550 3.34 0.52 9.30000
## 730 11.0 25.0 0.99660 3.36 0.76 10.10000
## 731 10.0 23.0 1.00315 2.92 0.74 11.10000
## 732 21.0 66.0 0.99740 3.25 0.56 9.20000
## 733 3.0 10.0 0.99572 3.02 0.83 10.90000
## 734 54.0 80.0 1.00020 3.30 0.76 10.70000
## 735 13.0 27.0 0.99362 3.57 0.50 11.90000
## 736 10.0 28.0 0.99830 3.45 0.78 9.50000
## 737 17.0 43.0 1.00140 3.06 0.80 10.00000
## 738 20.0 38.0 0.99818 3.24 0.71 10.80000
## 739 12.0 35.0 1.00060 3.10 0.56 9.00000
## 740 24.0 44.0 0.99534 3.40 0.85 11.00000
## 741 11.0 75.0 0.99736 3.20 0.59 9.20000
## 742 19.0 106.0 0.99270 3.54 0.62 12.20000
## 743 32.0 71.0 1.00015 3.31 0.71 9.80000
## 744 30.0 74.0 0.99865 3.30 0.64 10.40000
## 745 15.0 42.0 0.99780 3.31 0.64 9.00000
## 746 13.0 31.0 0.99538 3.36 0.54 10.50000
## 747 5.0 16.0 0.99360 3.50 0.44 11.90000
## 748 26.0 61.0 1.00025 3.60 0.72 9.80000
## 749 10.0 86.0 0.99830 3.37 0.62 9.50000
## 750 16.0 29.0 0.99471 3.32 0.51 9.80000
## 751 6.0 12.0 0.99605 3.52 0.63 10.70000
## 752 5.0 16.0 0.99720 3.15 0.65 11.00000
## 753 7.0 13.0 0.99639 3.38 0.56 10.80000
## 754 18.0 88.0 0.99745 3.53 0.55 9.50000
## 755 37.5 289.0 0.99316 3.01 0.51 12.30000
## 756 15.0 54.0 0.99700 3.26 0.65 9.80000
## 757 5.0 13.0 0.99700 3.41 0.57 11.00000
## 758 28.0 71.0 0.99970 3.50 0.57 9.70000
## 759 11.0 35.0 0.99557 3.36 0.62 10.80000
## 760 10.0 52.0 0.99680 3.23 0.77 9.50000
## 761 3.0 20.0 0.99540 3.34 0.58 9.40000
## 762 6.0 28.0 0.99704 3.07 0.65 10.03333
## 763 17.0 45.0 0.99760 3.46 0.54 9.50000
## 764 5.0 11.0 0.99478 3.19 0.46 11.40000
## 765 15.0 29.0 0.99472 3.23 0.76 11.30000
## 766 18.0 38.0 0.99518 3.16 0.85 11.10000
## 767 6.0 10.0 0.99536 3.31 0.68 11.20000
## 768 15.0 60.0 0.99638 3.31 0.54 9.80000
## 769 13.0 27.0 0.99500 3.32 0.90 13.40000
## 770 7.0 18.0 0.99470 3.32 0.79 14.00000
## 771 18.0 111.0 0.99820 3.30 0.60 9.50000
## 772 8.0 25.0 0.99746 3.69 0.73 10.50000
## 773 9.0 37.0 0.99732 3.66 0.65 9.80000
## 774 7.0 17.0 0.99840 3.08 0.67 9.30000
## 775 11.0 22.0 0.99630 3.26 0.50 9.50000
## 776 17.0 44.0 0.99734 3.28 0.77 11.50000
## 777 12.0 87.0 0.99780 3.33 0.83 10.50000
## 778 9.0 67.0 0.99680 3.17 0.53 9.40000
## 779 3.0 12.0 0.99640 3.53 0.49 10.90000
## 780 19.0 49.0 0.99666 3.40 0.76 10.90000
## 781 7.0 27.0 0.99870 3.69 0.91 9.40000
## 782 6.0 18.0 0.99348 3.57 0.54 11.95000
## 783 12.0 48.0 0.99888 3.31 0.54 9.50000
## 784 19.0 133.0 0.99720 2.93 1.98 9.80000
## 785 12.0 37.0 0.99820 3.17 0.67 9.60000
## 786 13.0 31.0 0.99538 3.36 0.54 10.50000
## 787 13.0 45.0 0.99720 3.48 0.64 9.20000
## 788 23.0 42.0 0.99356 3.34 0.72 12.90000
## 789 40.0 54.0 0.99500 3.54 0.93 10.70000
## 790 3.0 22.0 0.99630 3.25 0.63 9.20000
## 791 12.0 74.0 0.99738 3.14 0.54 9.40000
## 792 26.0 45.0 0.99824 3.38 0.64 10.10000
## 793 6.0 20.0 0.99630 3.29 0.60 10.20000
## 794 11.0 44.0 0.99530 3.47 0.55 10.40000
## 795 22.0 114.0 0.99700 3.25 0.73 9.20000
## 796 6.0 14.0 0.99702 3.21 0.49 11.80000
## 797 35.0 155.0 1.00100 3.41 0.67 11.20000
## 798 27.0 46.0 0.99592 3.02 0.47 9.20000
## 799 22.0 91.0 0.99790 3.29 0.62 10.10000
## 800 21.0 77.0 0.99800 3.28 0.51 9.80000
## 801 11.0 86.0 1.00100 3.43 0.64 11.30000
## 802 6.0 17.0 0.99760 3.17 0.47 10.00000
## 803 19.0 41.0 0.99939 3.21 0.76 11.30000
## 804 17.0 69.0 0.99734 3.26 0.63 10.20000
## 805 17.0 102.0 0.99780 3.35 0.80 10.50000
## 806 20.0 136.0 0.99720 2.93 1.95 9.90000
## 807 31.0 43.0 0.99371 3.41 0.57 11.80000
## 808 24.0 52.0 0.99494 3.34 0.71 11.20000
## 809 15.0 21.0 0.99572 3.38 0.60 11.30000
## 810 5.0 17.0 0.99760 3.30 1.05 9.40000
## 811 5.0 15.0 0.99700 3.37 0.56 11.30000
## 812 3.0 11.0 0.99378 3.71 0.63 12.80000
## 813 14.0 28.0 0.99820 3.03 0.93 9.80000
## 814 10.0 22.0 0.99760 3.22 0.44 9.60000
## 815 5.0 14.0 0.99820 3.17 0.42 10.00000
## 816 3.0 8.0 0.99534 3.15 0.73 11.40000
## 817 3.0 10.0 0.99586 3.21 0.59 12.10000
## 818 7.0 12.0 0.99587 3.34 0.39 9.50000
## 819 5.0 14.0 0.99870 3.29 0.52 10.70000
## 820 21.0 51.0 0.99820 3.16 0.72 11.50000
## 821 4.0 10.0 0.99356 3.33 0.70 12.80000
## 822 30.0 92.0 0.99892 3.20 0.58 9.20000
## 823 18.0 34.0 0.99480 3.39 0.60 10.60000
## 824 13.0 74.0 0.99580 3.26 0.54 9.90000
## 825 6.0 24.0 0.99780 3.15 0.90 11.00000
## 826 6.0 13.0 0.99774 3.22 0.76 11.40000
## 827 13.0 32.0 1.00000 3.28 0.56 10.00000
## 828 34.0 48.0 0.99832 3.38 0.86 9.90000
## 829 41.0 55.0 0.99652 3.47 0.73 10.90000
## 830 40.0 54.0 0.99683 3.40 0.73 9.70000
## 831 8.0 22.0 0.99740 3.22 0.94 10.90000
## 832 31.0 131.0 0.99622 3.21 0.52 9.90000
## 833 15.0 28.0 0.99444 3.78 0.61 12.50000
## 834 9.0 18.0 0.99720 3.46 0.65 9.20000
## 835 4.0 11.0 0.99592 3.53 0.64 10.90000
## 836 16.0 45.0 0.99702 3.03 1.34 9.20000
## 837 25.0 50.0 0.99478 3.20 0.83 10.90000
## 838 31.0 51.0 0.99672 3.53 0.81 10.40000
## 839 23.0 47.0 0.99572 3.58 0.50 11.20000
## 840 11.0 24.0 0.99498 3.27 0.64 12.10000
## 841 8.0 19.0 0.99630 3.56 0.73 10.60000
## 842 15.0 75.0 0.99980 3.18 0.64 9.40000
## 843 3.0 7.0 0.99470 3.19 0.55 11.40000
## 844 9.0 30.0 0.99680 3.41 0.53 9.50000
## 845 10.0 48.0 0.99760 3.34 0.46 9.40000
## 846 3.0 11.0 0.99577 3.23 0.57 13.20000
## 847 5.0 12.0 0.99580 3.34 0.56 9.20000
## 848 14.0 45.0 0.99336 3.38 0.54 11.00000
## 849 18.0 40.0 0.99760 3.14 0.51 9.80000
## 850 11.0 24.0 0.99745 3.29 0.99 12.00000
## 851 25.0 129.0 0.99720 3.08 0.53 9.00000
## 852 11.0 39.0 0.99680 3.42 0.58 9.60000
## 853 16.0 36.0 0.99564 3.38 0.60 10.30000
## 854 6.0 47.0 1.00140 3.01 0.81 10.80000
## 855 6.0 19.0 0.99860 3.27 0.82 11.70000
## 856 20.0 44.0 0.99650 3.38 0.59 10.70000
## 857 7.0 15.0 0.99810 3.18 0.62 9.50000
## 858 18.0 48.0 0.99940 3.22 0.64 10.30000
## 859 28.0 68.0 0.99501 3.36 0.66 11.20000
## 860 15.0 39.0 0.99560 3.56 0.52 12.70000
## 861 32.0 79.0 0.99640 3.30 0.72 11.00000
## 862 6.0 11.0 0.99318 3.35 0.66 12.70000
## 863 39.0 55.0 0.99560 3.39 0.84 11.40000
## 864 24.0 44.0 0.99534 3.40 0.85 11.00000
## 865 8.0 65.0 0.99340 3.90 0.56 13.10000
## 866 13.0 35.0 0.99720 3.30 0.59 9.00000
## 867 8.0 23.0 0.99650 3.45 0.56 10.70000
## 868 29.0 94.0 0.99786 3.14 0.58 9.10000
## 869 51.0 77.5 0.99558 3.20 0.45 9.50000
## 870 22.0 48.0 0.99494 3.30 0.84 11.50000
## 871 17.0 26.0 0.99783 3.45 0.53 9.50000
## 872 10.0 41.0 0.99740 3.24 0.71 9.80000
## 873 33.0 141.0 0.99620 3.25 0.51 9.90000
## 874 11.0 119.0 0.99538 3.36 0.70 10.90000
## 875 18.0 86.0 0.99680 3.59 0.57 9.30000
## 876 4.0 9.0 0.99458 3.16 0.54 9.80000
## 877 17.0 43.0 0.99444 3.31 0.77 12.50000
## 878 10.0 18.0 0.99684 3.37 0.68 11.20000
## 879 6.0 15.0 0.99880 2.99 0.87 10.20000
## 880 19.0 52.0 0.99800 3.44 0.64 9.20000
## 881 7.0 25.0 0.99628 3.24 0.44 10.40000
## 882 28.0 91.0 0.99520 3.44 0.55 12.10000
## 883 12.0 39.0 0.99770 3.50 0.70 9.90000
## 884 10.0 21.0 0.99576 3.15 0.49 9.90000
## 885 6.0 37.0 0.99920 3.05 0.56 10.00000
## 886 7.0 20.0 0.99590 3.20 0.56 9.60000
## 887 13.0 95.0 0.99700 3.16 0.54 9.10000
## 888 50.0 110.0 0.99830 3.26 0.77 9.40000
## 889 8.0 19.0 0.99740 3.27 0.73 9.30000
## 890 12.0 35.0 0.99680 3.38 0.62 9.50000
## 891 3.0 9.0 0.99719 3.33 0.57 10.30000
## 892 41.0 55.0 0.99652 3.47 0.73 10.90000
## 893 24.0 71.0 0.99624 3.27 0.85 11.00000
## 894 18.0 28.0 0.99765 3.41 0.60 9.40000
## 895 3.0 7.0 0.99454 3.22 0.58 11.20000
## 896 32.0 38.0 0.99438 3.32 0.58 11.40000
## 897 36.0 100.0 0.99064 3.26 0.39 11.70000
## 898 29.0 80.0 0.99020 3.49 0.66 13.60000
## 899 12.0 65.0 0.99670 3.29 0.51 9.20000
## 900 20.0 38.0 0.99780 3.40 0.53 9.50000
## 901 12.0 87.0 0.99780 3.33 0.83 10.50000
## 902 20.0 136.0 0.99720 2.93 1.95 9.90000
## 903 25.0 42.0 0.99530 3.24 0.77 12.60000
## 904 29.0 46.0 0.99430 3.20 0.60 12.20000
## 905 9.0 23.0 0.99638 3.23 0.76 12.00000
## 906 5.0 13.0 0.99760 3.23 0.82 12.60000
## 907 5.0 18.0 0.99720 3.21 0.87 11.30000
## 908 16.0 25.0 0.99360 3.28 0.66 12.40000
## 909 6.0 29.0 0.99870 2.88 0.82 9.80000
## 910 5.0 21.0 0.99860 3.17 0.53 10.50000
## 911 5.0 18.0 0.99666 3.20 0.52 9.40000
## 912 16.0 33.0 0.99736 3.58 0.69 10.80000
## 913 9.0 23.0 0.99680 3.30 1.20 10.30000
## 914 14.0 36.0 0.99364 3.41 0.56 12.60000
## 915 6.0 13.0 1.00000 3.17 0.71 9.50000
## 916 5.0 14.0 1.00020 3.19 0.44 9.60000
## 917 15.0 35.0 0.99590 3.36 0.59 9.70000
## 918 41.0 110.0 0.99820 3.08 0.61 9.20000
## 919 9.0 18.0 0.99680 3.36 0.57 9.50000
## 920 10.0 37.0 0.99660 3.17 0.91 9.50000
## 921 6.0 25.0 0.99980 3.12 0.59 9.30000
## 922 18.0 28.0 0.99483 3.55 0.66 10.90000
## 923 11.0 41.0 0.99670 3.36 0.55 9.10000
## 924 19.0 98.0 0.99815 3.32 0.63 9.50000
## 925 10.0 16.0 0.99479 3.43 0.59 11.50000
## 926 16.0 86.0 0.99743 3.53 0.57 9.70000
## 927 16.0 44.0 0.99580 3.13 0.52 11.00000
## 928 15.0 26.0 0.99538 3.57 0.63 12.00000
## 929 7.0 20.0 0.99647 3.32 0.63 10.50000
## 930 9.0 37.0 0.99732 3.66 0.65 9.80000
## 931 7.0 54.0 0.99660 3.36 0.52 9.40000
## 932 8.0 19.0 0.99701 3.31 0.53 10.00000
## 933 5.0 13.0 0.99420 3.72 0.58 11.40000
## 934 27.0 85.0 0.99706 3.41 0.58 9.00000
## 935 16.0 63.0 0.99650 3.19 0.82 9.60000
## 936 17.0 91.0 0.99616 3.26 0.58 9.80000
## 937 4.0 15.0 0.99693 3.22 0.55 10.30000
## 938 5.0 33.0 0.99612 3.37 0.58 11.00000
## 939 7.0 32.0 0.99668 3.39 0.54 9.60000
## 940 27.0 51.0 0.99634 3.49 0.64 10.40000
## 941 27.0 119.0 0.99720 3.16 1.12 9.10000
## 942 25.0 68.0 0.99950 3.15 0.82 10.40000
## 943 34.0 53.0 0.99580 3.41 0.67 9.70000
## 944 4.0 23.0 0.99710 3.15 0.74 9.20000
## 945 10.0 21.0 0.99692 3.29 0.67 10.00000
## 946 20.0 112.0 0.99680 3.21 0.71 9.30000
## 947 4.0 8.0 0.99396 3.33 0.37 10.40000
## 948 8.0 18.0 0.99344 3.39 0.56 12.40000
## 949 7.0 23.0 0.99974 3.21 0.69 10.90000
## 950 16.0 47.0 0.99594 3.40 0.78 11.30000
## 951 13.0 28.0 0.99660 3.52 0.62 10.20000
## 952 5.0 15.0 0.99870 2.98 0.70 9.20000
## 953 9.0 26.0 0.99470 3.36 0.60 11.90000
## 954 7.0 13.0 0.99582 3.53 0.58 10.80000
## 955 9.0 38.0 0.99616 3.15 0.53 9.80000
## 956 13.0 81.0 0.99688 3.24 0.54 9.50000
## 957 25.0 87.0 0.99975 3.24 0.62 9.70000
## 958 11.0 40.0 0.99780 3.39 0.61 10.00000
## 959 5.0 27.0 0.99340 3.57 0.84 12.50000
## 960 11.0 18.0 0.99191 3.45 0.56 12.20000
## 961 24.0 88.0 0.99612 3.30 0.53 9.80000
## 962 8.0 29.0 0.99855 3.67 0.73 9.60000
## 963 11.0 43.0 0.99750 3.53 0.61 10.50000
## 964 17.0 65.0 0.99294 3.33 0.60 10.50000
## 965 28.0 52.0 0.99378 3.49 0.52 11.60000
## 966 15.0 24.0 0.99438 3.40 0.82 11.20000
## 967 6.0 14.0 0.99940 3.19 0.62 9.50000
## 968 9.0 18.0 0.99444 3.42 0.69 11.30000
## 969 26.0 122.0 0.99801 3.22 1.07 9.70000
## 970 16.0 20.0 0.99672 3.61 0.82 10.00000
## 971 17.0 26.0 0.99575 3.36 0.61 10.20000
## 972 6.0 14.0 0.99780 3.34 0.52 10.00000
## 973 10.0 21.0 0.99552 3.41 0.76 11.90000
## 974 5.0 27.0 0.99990 3.08 0.87 10.90000
## 975 11.0 27.0 0.99740 3.21 0.80 9.40000
## 976 6.0 15.0 0.99730 3.09 0.66 11.80000
## 977 15.0 115.0 0.99800 3.23 0.59 9.50000
## 978 7.0 42.0 1.00220 3.07 0.73 10.00000
## 979 4.0 11.0 0.99620 3.41 0.39 10.90000
## 980 31.0 54.0 0.99728 3.30 0.65 10.00000
## 981 14.0 56.0 0.99620 3.21 0.60 10.90000
## 982 27.0 90.0 0.99840 3.16 0.70 9.40000
## 983 8.0 24.0 0.99578 2.88 0.47 9.70000
## 984 15.0 28.0 0.99459 3.42 0.70 10.00000
## 985 28.0 46.0 0.99930 3.20 0.80 10.20000
## 986 3.0 13.0 0.99600 3.23 1.10 10.00000
## 987 5.0 16.0 0.99649 3.27 0.74 12.30000
## 988 10.0 17.0 1.00020 3.07 0.56 11.70000
## 989 9.0 24.0 0.99695 3.22 0.82 10.30000
## 990 18.0 102.0 0.99670 3.26 0.59 9.30000
## 991 35.0 72.0 0.99700 3.44 0.52 9.40000
## 992 3.0 10.0 0.99572 3.02 0.83 10.90000
## 993 21.0 58.0 0.99970 3.46 0.72 10.20000
## 994 26.0 65.0 0.99716 3.46 0.62 9.50000
## 995 11.0 108.0 0.99640 3.15 0.66 9.80000
## 996 38.0 76.0 0.99900 3.17 0.85 12.00000
## 997 6.0 13.0 0.99643 3.22 0.65 11.60000
## 998 6.0 22.0 0.99860 3.17 0.66 11.20000
## 999 5.0 17.0 0.99960 3.22 0.62 11.20000
## 1000 23.0 71.0 0.99820 3.52 0.65 9.70000
## 1001 26.0 44.0 0.99522 3.42 0.68 10.80000
## 1002 9.0 59.0 0.99976 3.28 0.54 9.60000
## 1003 24.0 120.0 0.99680 3.25 0.54 9.40000
## 1004 16.0 39.0 0.99610 3.34 0.55 9.20000
## 1005 32.0 79.0 0.99640 3.30 0.72 11.00000
## 1006 9.0 29.0 0.99740 3.26 1.56 9.10000
## 1007 17.0 24.0 0.99437 3.59 0.55 11.20000
## 1008 18.0 64.0 0.99652 2.90 1.33 9.10000
## 1009 6.0 20.0 0.99770 3.18 1.06 11.00000
## 1010 29.0 53.0 0.99980 3.24 0.67 9.90000
## 1011 10.0 38.0 0.99735 3.31 0.64 9.40000
## 1012 6.0 15.0 0.99840 3.01 0.83 11.80000
## 1013 16.0 43.0 0.99800 3.48 0.64 9.10000
## 1014 19.0 40.0 0.99387 3.38 0.66 12.60000
## 1015 6.0 32.0 1.00060 3.12 0.78 10.70000
## 1016 7.0 21.0 0.99920 3.17 0.84 12.20000
## 1017 15.0 21.0 0.99572 3.38 0.60 11.30000
## 1018 36.0 60.0 0.99494 3.10 0.40 9.30000
## 1019 14.0 31.0 0.99689 3.59 0.66 9.80000
## 1020 4.0 10.0 0.99356 3.33 0.70 12.80000
## 1021 12.0 53.0 0.99710 3.36 0.57 9.10000
## 1022 8.0 32.0 0.99860 2.89 0.50 9.60000
## 1023 23.0 81.0 1.00020 3.48 0.74 11.60000
## 1024 13.0 50.0 1.00150 3.16 0.69 9.20000
## 1025 4.0 10.0 0.99710 3.04 0.63 9.60000
## 1026 3.0 8.0 0.99642 3.20 0.53 11.90000
## 1027 6.0 14.0 0.99562 3.51 0.66 10.80000
## 1028 13.0 27.0 0.99394 3.14 0.59 11.30000
## 1029 20.0 56.0 0.99672 3.35 0.56 9.20000
## 1030 15.0 59.0 0.99640 3.30 0.46 9.40000
## 1031 11.0 47.0 0.99599 3.27 0.81 11.00000
## 1032 5.0 15.0 0.99910 3.32 0.60 9.00000
## 1033 12.0 29.0 0.99700 3.24 0.75 11.70000
## 1034 7.0 19.0 0.99800 3.31 0.88 10.50000
## 1035 3.0 6.0 0.99690 3.13 0.63 10.40000
## 1036 5.0 22.0 0.99860 3.37 0.58 10.30000
## 1037 7.0 31.0 0.99760 3.27 0.60 9.30000
## 1038 5.0 10.0 0.99418 3.28 0.57 11.80000
## 1039 18.0 22.0 0.99573 3.21 0.68 9.90000
## 1040 16.0 40.0 0.99910 3.39 0.62 9.40000
## 1041 40.0 63.0 0.99790 3.60 0.81 9.90000
## 1042 14.0 96.0 0.99610 3.19 0.62 9.50000
## 1043 7.0 15.0 0.99458 3.32 0.80 11.90000
## 1044 19.0 72.0 0.99543 3.39 0.72 11.80000
## 1045 30.0 92.0 0.99892 3.20 0.58 9.20000
## 1046 5.0 14.0 0.99854 3.36 0.53 9.60000
## 1047 16.0 40.0 0.99910 3.39 0.62 9.40000
## 1048 31.0 72.0 0.99960 3.10 0.73 10.50000
## 1049 6.0 47.0 1.00210 3.30 0.68 12.70000
## 1050 7.0 19.0 0.99400 3.50 0.48 9.80000
## 1051 5.0 14.0 0.99660 3.63 0.54 10.70000
## 1052 35.0 70.0 0.99588 3.43 0.62 10.10000
## 1053 32.0 98.0 0.99578 3.33 0.62 9.80000
## 1054 4.0 12.0 0.99473 3.30 0.76 12.80000
## 1055 4.0 23.0 0.99960 3.28 0.97 10.10000
## 1056 17.0 43.0 1.00140 3.06 0.80 10.00000
## 1057 3.0 15.0 0.99580 3.41 0.56 9.20000
## 1058 10.0 103.0 0.99690 2.87 1.36 10.20000
## 1059 8.0 23.0 0.99610 3.30 0.58 9.60000
## 1060 34.0 85.0 0.99668 3.19 0.42 9.20000
## 1061 2.0 45.0 0.99727 3.31 0.91 9.50000
## 1062 14.0 30.0 0.99660 3.52 0.55 10.70000
## 1063 16.0 63.0 0.99650 3.19 0.82 9.60000
## 1064 13.0 50.0 0.99570 3.38 0.55 9.20000
## 1065 4.0 25.0 0.99534 3.38 0.45 9.60000
## 1066 17.0 26.0 0.99520 3.40 0.58 9.80000
## 1067 25.0 99.0 0.99500 3.35 0.54 10.10000
## 1068 9.0 30.0 0.99824 3.34 0.64 10.50000
## 1069 12.0 35.0 1.00060 3.10 0.56 9.00000
## 1070 24.0 52.0 0.99752 3.35 0.94 10.00000
## 1071 15.0 65.0 0.99590 3.28 0.54 9.20000
## 1072 19.0 47.0 1.00030 3.26 1.11 11.00000
## 1073 13.0 31.0 0.99538 3.36 0.54 10.50000
## 1074 6.0 14.0 0.99820 3.30 0.75 9.80000
## 1075 14.0 56.0 0.99620 3.21 0.60 10.90000
## 1076 26.0 35.0 0.99314 3.32 0.82 11.60000
## 1077 16.0 24.0 0.99376 3.56 0.77 11.10000
## 1078 10.0 21.0 0.99760 2.98 0.66 9.90000
## 1079 18.0 42.0 0.99549 3.39 0.66 11.00000
## 1080 34.0 53.0 0.99580 3.41 0.67 9.70000
## 1081 3.0 13.0 0.99557 3.62 0.62 11.50000
## 1082 21.0 55.0 0.99495 3.18 0.82 11.00000
## 1083 14.0 27.0 0.99598 3.06 0.84 11.40000
## 1084 12.0 20.0 0.99546 3.29 0.54 10.10000
## 1085 14.0 44.0 0.99800 3.12 0.74 10.00000
## 1086 11.0 32.0 0.99604 3.23 0.48 10.00000
## 1087 17.0 56.0 0.99690 3.04 1.08 9.20000
## 1088 10.0 41.0 0.99800 3.23 0.66 12.50000
## 1089 16.0 47.0 0.99860 3.12 1.02 10.60000
## 1090 6.0 23.0 1.00260 3.12 0.66 9.20000
## 1091 9.0 32.0 0.99740 3.13 0.55 9.50000
## 1092 6.0 16.0 0.99800 3.18 0.60 9.50000
## 1093 6.0 15.0 0.99620 3.44 0.58 10.70000
## 1094 3.0 8.0 0.99471 3.16 0.51 11.80000
## 1095 15.0 85.0 0.99746 3.51 0.54 9.50000
## 1096 18.0 35.0 0.99680 3.44 0.63 10.00000
## 1097 15.0 23.0 0.99627 3.54 0.60 11.00000
## 1098 6.0 16.0 0.99680 3.22 0.72 11.20000
## 1099 21.0 59.0 0.99890 3.37 0.71 11.50000
## 1100 21.0 45.0 0.99740 3.49 0.96 12.10000
## 1101 33.0 75.0 0.99680 3.40 0.58 9.50000
## 1102 7.0 12.0 0.99587 3.34 0.39 9.50000
## 1103 12.0 29.0 0.99970 3.11 1.36 9.80000
## 1104 15.0 40.0 0.99414 3.22 0.60 12.20000
## 1105 15.0 63.0 0.99640 3.00 1.59 9.50000
## 1106 27.0 85.0 0.99706 3.41 0.58 9.00000
## 1107 32.0 59.0 0.99617 3.33 0.77 12.00000
## 1108 10.0 21.0 0.99552 3.41 0.76 11.90000
## 1109 31.0 131.0 0.99622 3.21 0.52 9.90000
## 1110 37.0 53.0 0.99374 3.35 0.76 11.60000
## 1111 10.0 24.0 0.99523 3.27 0.85 11.70000
## 1112 11.0 22.0 1.00000 3.00 0.70 9.30000
## 1113 4.0 11.0 0.99745 3.31 0.65 9.55000
## 1114 11.0 26.0 0.99940 3.35 0.83 9.40000
## 1115 13.0 49.0 0.99880 3.18 0.65 11.00000
## 1116 51.0 77.5 0.99558 3.20 0.45 9.50000
## 1117 24.0 148.0 0.99480 3.16 0.57 11.30000
## 1118 13.0 26.0 0.99780 3.37 0.46 9.60000
## 1119 16.0 89.0 0.99182 3.54 0.88 13.60000
## 1120 9.0 31.0 0.99660 3.39 0.64 9.60000
## 1121 21.0 75.0 0.99870 3.35 0.57 9.70000
## 1122 29.0 63.0 0.99615 3.37 0.58 10.50000
## 1123 8.0 16.0 0.99334 3.43 0.52 12.60000
## 1124 3.0 14.0 0.99702 3.27 0.78 10.90000
## 1125 12.0 19.0 0.99570 3.17 0.55 10.40000
## 1126 5.0 16.0 0.99720 3.15 0.65 11.00000
## 1127 45.0 87.0 0.99830 3.48 0.53 10.00000
## 1128 6.0 13.0 0.99664 3.59 0.61 10.00000
## 1129 21.0 94.0 0.99440 3.54 0.52 10.00000
## 1130 6.0 12.0 0.99542 3.35 0.61 10.70000
## 1131 4.0 8.0 0.99510 3.40 0.64 11.00000
## 1132 9.0 92.0 0.99630 3.28 0.62 9.40000
## 1133 6.0 19.0 0.99550 3.42 0.57 9.40000
## 1134 13.0 30.0 0.99680 3.23 0.73 9.70000
## 1135 6.0 12.0 0.99630 3.52 0.50 12.20000
## 1136 8.0 32.0 0.99860 2.89 0.50 9.60000
## 1137 10.0 37.0 1.00030 3.32 0.91 11.00000
## 1138 9.0 22.0 0.99606 3.34 0.60 9.70000
## 1139 30.0 109.0 0.99980 3.27 0.57 9.30000
## 1140 11.0 27.0 0.99740 3.21 0.80 9.40000
## 1141 20.0 38.0 0.99490 3.29 0.47 10.80000
## 1142 5.0 17.0 0.99683 3.17 0.65 10.60000
## 1143 16.0 86.0 0.99743 3.53 0.57 9.70000
## 1144 5.0 13.0 0.99472 3.52 0.56 11.40000
## 1145 28.0 83.0 0.99840 3.28 0.82 9.60000
## 1146 21.0 56.0 0.99633 3.52 0.60 9.50000
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## 1199 0test_raw.df <- as.data.frame(df_new[-train_df_raw, ])
test_raw.df## fixed acidity volatile acidity citric acid residual sugar chlorides
## 1 7.4 0.660 0.00 1.80 0.075
## 2 7.3 0.650 0.00 1.20 0.065
## 3 5.6 0.615 0.00 1.60 0.089
## 4 7.4 0.590 0.08 4.40 0.086
## 5 8.9 0.220 0.48 1.80 0.077
## 6 7.9 0.430 0.21 1.60 0.106
## 7 7.1 0.710 0.00 1.90 0.080
## 8 6.7 0.675 0.07 2.40 0.089
## 9 7.8 0.600 0.14 2.40 0.086
## 10 5.6 0.310 0.37 1.40 0.074
## 11 6.6 0.500 0.04 2.10 0.068
## 12 7.0 0.735 0.05 2.00 0.081
## 13 7.7 0.670 0.23 2.10 0.088
## 14 7.7 0.690 0.22 1.90 0.084
## 15 8.8 0.410 0.64 2.20 0.093
## 16 7.3 0.670 0.26 1.80 0.401
## 17 6.3 0.300 0.48 1.80 0.069
## 18 6.9 0.550 0.15 2.20 0.076
## 19 9.3 0.390 0.44 2.10 0.107
## 20 7.9 0.520 0.26 1.90 0.079
## 21 8.3 0.610 0.30 2.10 0.084
## 22 8.1 0.545 0.18 1.90 0.080
## 23 7.2 0.490 0.24 2.20 0.070
## 24 8.1 0.575 0.22 2.10 0.077
## 25 6.2 0.630 0.31 1.70 0.088
## 26 8.1 0.785 0.52 2.00 0.122
## 27 7.0 0.690 0.08 1.80 0.097
## 28 8.1 1.330 0.00 1.80 0.082
## 29 7.9 1.040 0.05 2.20 0.084
## 30 8.3 0.715 0.15 1.80 0.089
## 31 7.8 0.560 0.19 2.00 0.081
## 32 8.1 0.670 0.55 1.80 0.117
## 33 6.9 0.490 0.10 2.30 0.074
## 34 7.1 0.430 0.42 5.50 0.070
## 35 7.1 0.430 0.42 5.50 0.070
## 36 6.8 0.640 0.10 2.10 0.085
## 37 7.3 0.550 0.03 1.60 0.072
## 38 7.4 0.620 0.05 1.90 0.068
## 39 7.3 0.380 0.21 2.00 0.080
## 40 6.9 0.500 0.04 1.50 0.085
## 41 7.0 0.805 0.00 2.50 0.068
## 42 8.9 0.610 0.49 2.00 0.270
## 43 6.7 0.620 0.21 1.90 0.079
## 44 7.9 0.500 0.33 2.00 0.084
## 45 6.4 0.370 0.25 1.90 0.074
## 46 7.6 0.550 0.21 2.20 0.071
## 47 7.6 0.550 0.21 2.20 0.071
## 48 11.5 0.300 0.60 2.00 0.067
## 49 6.9 1.090 0.06 2.10 0.061
## 50 7.0 0.430 0.36 1.60 0.089
## 51 7.8 0.570 0.31 1.80 0.069
## 52 7.0 0.490 0.49 5.60 0.060
## 53 7.5 0.490 0.19 1.90 0.076
## 54 6.8 0.610 0.04 1.50 0.057
## 55 8.4 0.635 0.36 2.00 0.089
## 56 8.9 0.590 0.50 2.00 0.337
## 57 8.5 0.370 0.20 2.80 0.090
## 58 7.2 0.630 0.00 1.90 0.097
## 59 7.7 0.580 0.10 1.80 0.102
## 60 15.0 0.210 0.44 2.20 0.075
## 61 7.1 0.680 0.07 1.90 0.075
## 62 8.2 0.600 0.17 2.30 0.072
## 63 7.7 0.530 0.06 1.70 0.074
## 64 7.7 0.775 0.42 1.90 0.092
## 65 7.7 0.410 0.76 1.80 0.611
## 66 7.9 0.545 0.06 4.00 0.087
## 67 10.9 0.370 0.58 4.00 0.071
## 68 8.9 0.400 0.32 5.60 0.087
## 69 7.7 0.270 0.68 3.50 0.358
## 70 8.9 0.400 0.32 5.60 0.087
## 71 7.5 0.400 0.12 3.00 0.092
## 72 6.9 0.360 0.25 2.40 0.098
## 73 6.9 0.670 0.06 2.10 0.080
## 74 7.4 0.670 0.12 1.60 0.186
## 75 8.4 0.650 0.60 2.10 0.112
## 76 8.6 0.470 0.30 3.00 0.076
## 77 7.1 0.350 0.29 2.50 0.096
## 78 9.8 0.660 0.39 3.20 0.083
## 79 7.8 0.620 0.05 2.30 0.079
## 80 7.9 0.650 0.01 2.50 0.078
## 81 12.4 0.490 0.58 3.00 0.103
## 82 12.2 0.340 0.50 2.40 0.066
## 83 6.6 0.815 0.02 2.70 0.072
## 84 13.8 0.490 0.67 3.00 0.093
## 85 9.6 0.560 0.31 2.80 0.089
## 86 10.7 0.670 0.22 2.70 0.107
## 87 13.5 0.530 0.79 4.80 0.120
## 88 6.7 0.750 0.01 2.40 0.078
## 89 12.6 0.380 0.66 2.60 0.088
## 90 8.2 0.700 0.23 2.00 0.099
## 91 10.0 0.420 0.50 3.40 0.107
## 92 12.8 0.615 0.66 5.80 0.083
## 93 8.3 0.260 0.42 2.00 0.080
## 94 7.8 0.460 0.26 1.90 0.088
## 95 12.0 0.370 0.76 4.20 0.066
## 96 11.4 0.600 0.49 2.70 0.085
## 97 7.7 0.690 0.05 2.70 0.075
## 98 12.5 0.460 0.49 4.50 0.070
## 99 9.0 0.430 0.34 2.50 0.080
## 100 9.1 0.450 0.35 2.40 0.080
## 101 7.1 0.735 0.16 1.90 0.100
## 102 10.6 0.480 0.64 2.20 0.111
## 103 6.6 0.840 0.03 2.30 0.059
## 104 7.7 0.960 0.20 2.00 0.047
## 105 6.6 0.840 0.03 2.30 0.059
## 106 12.8 0.840 0.63 2.40 0.088
## 107 11.9 0.370 0.69 2.30 0.078
## 108 8.0 0.670 0.30 2.00 0.060
## 109 7.0 0.620 0.18 1.50 0.062
## 110 15.6 0.685 0.76 3.70 0.100
## 111 9.5 0.735 0.10 2.10 0.079
## 112 8.6 0.530 0.22 2.00 0.100
## 113 8.4 0.370 0.53 1.80 0.413
## 114 10.4 0.330 0.63 2.80 0.084
## 115 7.0 0.230 0.40 1.60 0.063
## 116 11.6 0.580 0.66 2.20 0.074
## 117 11.0 0.260 0.68 2.55 0.085
## 118 9.6 0.680 0.24 2.20 0.087
## 119 9.3 0.270 0.41 2.00 0.091
## 120 10.6 0.360 0.60 2.20 0.152
## 121 10.2 0.670 0.39 1.90 0.054
## 122 9.3 0.775 0.27 2.80 0.078
## 123 8.7 0.690 0.31 3.00 0.086
## 124 7.8 0.520 0.25 1.90 0.081
## 125 7.8 0.520 0.25 1.90 0.081
## 126 10.4 0.440 0.73 6.55 0.074
## 127 10.5 0.260 0.47 1.90 0.078
## 128 10.4 0.240 0.46 1.80 0.075
## 129 10.0 0.590 0.31 2.20 0.090
## 130 12.4 0.420 0.49 4.60 0.073
## 131 10.5 0.510 0.64 2.40 0.107
## 132 8.5 0.655 0.49 6.10 0.122
## 133 10.9 0.210 0.49 2.80 0.088
## 134 7.3 0.365 0.49 2.50 0.088
## 135 9.8 0.250 0.49 2.70 0.088
## 136 9.1 0.220 0.24 2.10 0.078
## 137 10.3 0.270 0.24 2.10 0.072
## 138 8.1 0.825 0.24 2.10 0.084
## 139 12.9 0.350 0.49 5.80 0.066
## 140 11.2 0.660 0.24 2.50 0.085
## 141 12.4 0.350 0.49 2.60 0.079
## 142 9.0 0.530 0.49 1.90 0.171
## 143 5.0 1.040 0.24 1.60 0.050
## 144 15.5 0.645 0.49 4.20 0.095
## 145 10.9 0.530 0.49 4.60 0.118
## 146 9.0 0.440 0.49 2.40 0.078
## 147 7.6 0.290 0.49 2.70 0.092
## 148 13.0 0.470 0.49 4.30 0.085
## 149 12.7 0.600 0.49 2.80 0.075
## 150 6.2 0.360 0.24 2.20 0.095
## 151 10.2 0.240 0.49 2.40 0.075
## 152 8.8 0.470 0.49 2.90 0.085
## 153 5.0 0.420 0.24 2.00 0.060
## 154 9.0 0.450 0.49 2.60 0.084
## 155 6.6 0.390 0.49 1.70 0.070
## 156 9.9 0.490 0.58 3.50 0.094
## 157 11.9 0.580 0.58 1.90 0.071
## 158 8.8 0.480 0.41 3.30 0.092
## 159 8.8 0.240 0.54 2.50 0.083
## 160 11.4 0.460 0.50 2.70 0.122
## 161 11.3 0.370 0.41 2.30 0.088
## 162 8.3 0.540 0.24 3.40 0.076
## 163 8.8 0.600 0.29 2.20 0.098
## 164 7.6 0.685 0.23 2.30 0.111
## 165 10.4 0.280 0.54 2.70 0.105
## 166 7.6 0.410 0.14 3.00 0.087
## 167 9.6 0.880 0.28 2.40 0.086
## 168 7.3 0.670 0.05 3.60 0.107
## 169 8.3 0.845 0.01 2.20 0.070
## 170 15.9 0.360 0.65 7.50 0.096
## 171 9.4 0.330 0.59 2.80 0.079
## 172 7.2 0.570 0.06 1.60 0.076
## 173 10.1 0.280 0.46 1.80 0.050
## 174 11.3 0.340 0.45 2.00 0.082
## 175 9.8 0.980 0.32 2.30 0.078
## 176 7.1 0.650 0.18 1.80 0.070
## 177 7.7 0.660 0.04 1.60 0.039
## 178 7.0 0.650 0.02 2.10 0.066
## 179 11.8 0.380 0.55 2.10 0.071
## 180 7.0 0.650 0.02 2.10 0.066
## 181 7.4 0.490 0.19 3.00 0.077
## 182 7.8 0.545 0.12 2.50 0.068
## 183 9.7 0.310 0.47 1.60 0.062
## 184 10.6 1.025 0.43 2.80 0.080
## 185 8.9 0.565 0.34 3.00 0.093
## 186 6.4 0.570 0.02 1.80 0.067
## 187 7.0 0.450 0.34 2.70 0.082
## 188 7.7 0.965 0.10 2.10 0.112
## 189 11.6 0.410 0.58 2.80 0.096
## 190 7.3 0.510 0.18 2.10 0.070
## 191 8.3 0.650 0.10 2.90 0.089
## 192 8.3 0.650 0.10 2.90 0.089
## 193 7.8 0.480 0.68 1.70 0.415
## 194 6.3 0.980 0.01 2.00 0.057
## 195 8.1 0.870 0.00 2.20 0.084
## 196 8.1 0.870 0.00 2.20 0.084
## 197 7.5 0.600 0.32 2.70 0.103
## 198 9.5 0.570 0.27 2.30 0.082
## 199 7.9 0.400 0.29 1.80 0.157
## 200 7.2 1.000 0.00 3.00 0.102
## 201 7.0 0.570 0.00 2.00 0.190
## 202 9.9 0.350 0.41 2.30 0.083
## 203 8.8 0.640 0.17 2.90 0.084
## 204 10.1 0.270 0.54 2.30 0.065
## 205 9.3 0.370 0.44 1.60 0.038
## 206 7.2 0.610 0.08 4.00 0.082
## 207 8.6 0.550 0.09 3.30 0.068
## 208 6.9 0.390 0.24 2.10 0.102
## 209 6.7 0.540 0.13 2.00 0.076
## 210 6.7 0.540 0.13 2.00 0.076
## 211 7.1 0.480 0.28 2.80 0.068
## 212 7.1 0.460 0.14 2.80 0.076
## 213 7.1 0.460 0.14 2.80 0.076
## 214 7.6 0.665 0.10 1.50 0.066
## 215 6.7 0.280 0.28 2.40 0.012
## 216 6.0 0.500 0.04 2.20 0.092
## 217 6.4 0.640 0.21 1.80 0.081
## 218 9.3 0.430 0.44 1.90 0.085
## 219 9.3 0.360 0.39 1.50 0.080
## 220 7.2 0.620 0.06 2.50 0.078
## 221 7.2 0.635 0.07 2.60 0.077
## 222 6.9 0.510 0.23 2.00 0.072
## 223 6.8 0.560 0.22 1.80 0.074
## 224 9.1 0.210 0.37 1.60 0.067
## 225 8.8 0.310 0.40 2.80 0.109
## 226 7.7 0.715 0.01 2.10 0.064
## 227 8.4 0.310 0.29 3.10 0.194
## 228 9.0 0.800 0.12 2.40 0.083
## 229 7.2 0.340 0.24 2.00 0.071
## 230 7.2 0.630 0.03 2.20 0.080
## 231 7.1 0.590 0.01 2.30 0.080
## 232 8.3 0.310 0.39 2.40 0.078
## 233 6.1 0.560 0.00 2.20 0.079
## 234 10.0 0.460 0.44 2.90 0.065
## 235 5.3 0.715 0.19 1.50 0.161
## 236 8.4 0.620 0.12 1.80 0.072
## 237 9.6 0.410 0.37 2.30 0.091
## 238 6.8 0.410 0.31 8.80 0.084
## 239 8.6 0.470 0.27 2.30 0.055
## 240 8.8 0.300 0.38 2.30 0.060
## 241 6.2 0.460 0.17 1.60 0.073
## 242 9.6 0.330 0.52 2.20 0.074
## 243 10.1 0.430 0.40 2.60 0.092
## 244 8.3 0.280 0.48 2.10 0.093
## 245 9.0 0.360 0.52 2.10 0.111
## 246 9.5 0.370 0.52 2.00 0.088
## 247 7.1 0.560 0.14 1.60 0.078
## 248 6.6 0.570 0.02 2.10 0.115
## 249 8.4 0.590 0.29 2.60 0.109
## 250 9.5 0.860 0.26 1.90 0.079
## 251 7.3 0.520 0.32 2.10 0.070
## 252 7.7 0.390 0.12 1.70 0.097
## 253 7.1 0.340 0.28 2.00 0.082
## 254 10.0 0.350 0.45 2.50 0.092
## 255 9.9 0.350 0.38 1.50 0.058
## 256 6.8 0.360 0.32 1.80 0.067
## 257 7.0 0.500 0.14 1.80 0.078
## 258 7.0 0.510 0.09 2.10 0.062
## 259 7.2 0.340 0.21 2.50 0.075
## 260 8.9 0.745 0.18 2.50 0.077
## 261 7.6 0.780 0.00 1.70 0.076
## 262 7.1 0.430 0.17 1.80 0.082
## 263 8.3 0.330 0.42 2.30 0.070
## 264 9.9 0.530 0.57 2.40 0.093
## 265 11.6 0.230 0.57 1.80 0.074
## 266 8.0 0.380 0.44 1.90 0.098
## 267 6.6 0.520 0.08 2.40 0.070
## 268 8.0 0.620 0.35 2.80 0.086
## 269 9.3 0.330 0.45 1.50 0.057
## 270 9.1 0.250 0.34 2.00 0.071
## 271 8.6 0.370 0.65 6.40 0.080
## 272 7.9 0.300 0.68 8.30 0.050
## 273 8.5 0.340 0.40 4.70 0.055
## 274 11.6 0.410 0.54 1.50 0.095
## 275 7.9 0.340 0.42 2.00 0.086
## 276 6.6 0.725 0.09 5.50 0.117
## 277 8.4 0.340 0.42 2.10 0.072
## 278 7.4 0.490 0.27 2.10 0.071
## 279 5.4 0.420 0.27 2.00 0.092
## 280 7.0 0.690 0.07 2.50 0.091
## 281 5.6 0.660 0.00 2.50 0.066
## 282 7.9 0.540 0.34 2.50 0.076
## 283 10.7 0.400 0.37 1.90 0.081
## 284 5.8 0.290 0.26 1.70 0.063
## 285 9.1 0.600 0.00 1.90 0.058
## 286 8.0 0.250 0.43 1.70 0.067
## 287 10.4 0.520 0.45 2.00 0.080
## 288 6.9 0.450 0.11 2.40 0.043
## 289 7.8 0.500 0.12 1.80 0.178
## 290 6.6 0.580 0.00 2.20 0.100
## 291 5.1 0.510 0.18 2.10 0.042
## 292 9.0 0.785 0.24 1.70 0.078
## 293 9.9 0.540 0.26 2.00 0.111
## 294 8.2 0.330 0.39 2.50 0.074
## 295 6.5 0.340 0.27 2.80 0.067
## 296 8.2 0.350 0.33 2.40 0.076
## 297 10.2 0.400 0.40 2.50 0.068
## 298 7.0 0.430 0.30 2.00 0.085
## 299 6.7 0.640 0.23 2.10 0.080
## 300 8.8 0.955 0.05 1.80 0.075
## 301 7.2 0.360 0.46 2.10 0.074
## 302 6.8 0.650 0.02 2.10 0.078
## 303 8.8 0.270 0.46 2.10 0.095
## 304 8.2 0.340 0.37 1.90 0.057
## 305 8.2 0.400 0.31 1.90 0.082
## 306 10.9 0.320 0.52 1.80 0.132
## 307 8.1 0.530 0.22 2.20 0.078
## 308 9.0 0.580 0.25 2.00 0.104
## 309 7.6 0.430 0.29 2.10 0.075
## 310 7.6 0.430 0.29 2.10 0.075
## 311 7.1 0.750 0.01 2.20 0.059
## 312 6.5 0.670 0.00 4.30 0.057
## 313 7.4 0.550 0.19 1.80 0.082
## 314 7.9 0.660 0.00 1.40 0.096
## 315 7.8 0.700 0.06 1.90 0.079
## 316 6.8 0.640 0.00 2.70 0.123
## 317 8.6 0.635 0.68 1.80 0.403
## 318 5.5 0.490 0.03 1.80 0.044
## 319 5.9 0.460 0.00 1.90 0.077
## 320 7.8 0.580 0.13 2.10 0.102
## 321 8.5 0.400 0.40 6.30 0.050
## 322 9.8 0.300 0.39 1.70 0.062
## 323 7.1 0.460 0.20 1.90 0.077
## 324 11.3 0.370 0.50 1.80 0.090
## 325 7.2 0.530 0.14 2.10 0.064
## 326 6.8 0.680 0.09 3.90 0.068
## 327 7.0 0.620 0.10 1.40 0.071
## 328 6.5 0.510 0.15 3.00 0.064
## 329 8.0 1.180 0.21 1.90 0.083
## 330 9.1 0.340 0.42 1.80 0.058
## 331 6.7 0.460 0.24 1.70 0.077
## 332 6.7 0.460 0.24 1.70 0.077
## 333 11.5 0.420 0.48 2.60 0.077
## 334 9.0 0.600 0.29 2.00 0.069
## 335 7.2 0.620 0.01 2.30 0.065
## 336 7.6 0.645 0.03 1.90 0.086
## 337 7.2 0.580 0.03 2.30 0.077
## 338 6.1 0.320 0.25 1.80 0.086
## 339 7.3 0.430 0.24 2.50 0.078
## 340 7.2 0.605 0.02 1.90 0.096
## 341 7.3 0.740 0.08 1.70 0.094
## 342 8.7 0.780 0.51 1.70 0.415
## 343 7.7 0.750 0.27 3.80 0.110
## 344 8.2 0.885 0.20 1.40 0.086
## 345 5.2 0.490 0.26 2.30 0.090
## 346 7.1 0.755 0.15 1.80 0.107
## 347 6.7 0.480 0.02 2.20 0.080
## 348 8.0 0.520 0.25 2.00 0.078
## 349 7.2 0.670 0.00 2.20 0.068
## 350 8.0 0.500 0.39 2.60 0.082
## 351 7.7 0.280 0.30 2.00 0.062
## 352 6.0 0.510 0.00 2.10 0.064
## 353 8.2 0.240 0.34 5.10 0.062
## 354 6.2 0.580 0.00 1.60 0.065
## 355 10.0 0.320 0.59 2.20 0.077
## 356 7.3 0.340 0.33 2.50 0.064
## 357 7.7 0.640 0.21 2.20 0.077
## 358 7.8 0.530 0.01 1.60 0.077
## 359 7.7 0.230 0.37 1.80 0.046
## 360 6.1 0.400 0.16 1.80 0.069
## 361 10.2 0.540 0.37 15.40 0.214
## 362 7.3 0.670 0.02 2.20 0.072
## 363 7.4 0.785 0.19 5.20 0.094
## 364 7.3 0.670 0.02 2.20 0.072
## 365 6.8 0.670 0.00 1.90 0.080
## 366 7.8 0.320 0.44 2.70 0.104
## 367 6.0 0.540 0.06 1.80 0.050
## 368 6.7 1.040 0.08 2.30 0.067
## 369 5.3 0.470 0.11 2.20 0.048
## 370 5.6 0.620 0.03 1.50 0.080
## 371 7.0 0.655 0.16 2.10 0.074
## 372 6.2 0.570 0.10 2.10 0.048
## 373 7.7 0.540 0.26 1.90 0.089
## 374 7.5 0.725 0.04 1.50 0.076
## 375 7.8 0.820 0.29 4.30 0.083
## 376 6.9 0.840 0.21 4.10 0.074
## 377 6.9 0.840 0.21 4.10 0.074
## 378 6.1 0.320 0.25 2.30 0.071
## 379 6.8 0.480 0.25 2.00 0.076
## 380 6.0 0.420 0.19 2.00 0.075
## 381 7.1 0.690 0.08 2.10 0.063
## 382 8.0 0.390 0.30 1.90 0.074
## 383 5.4 0.580 0.08 1.90 0.059
## 384 7.2 0.390 0.32 1.80 0.065
## 385 11.1 0.440 0.42 2.20 0.064
## 386 8.4 0.370 0.43 2.30 0.063
## 387 11.2 0.400 0.50 2.00 0.099
## 388 7.4 0.360 0.30 1.80 0.074
## 389 6.6 0.880 0.04 2.20 0.066
## 390 6.9 0.630 0.33 6.70 0.235
## 391 7.2 0.695 0.13 2.00 0.076
## 392 7.0 0.560 0.13 1.60 0.077
## 393 6.4 0.360 0.53 2.20 0.230
## 394 7.3 0.690 0.32 2.20 0.069
## 395 6.2 0.700 0.15 5.10 0.076
## 396 6.7 0.320 0.44 2.40 0.061
## 397 7.5 0.310 0.41 2.40 0.065
## 398 6.6 0.725 0.20 7.80 0.073
## 399 5.4 0.740 0.09 1.70 0.089
## 400 6.3 0.510 0.13 2.30 0.076
## free sulfur dioxide total sulfur dioxide density pH sulphates alcohol
## 1 13.0 40 0.99780 3.51 0.56 9.400000
## 2 15.0 21 0.99460 3.39 0.47 10.000000
## 3 16.0 59 0.99430 3.58 0.52 9.900000
## 4 6.0 29 0.99740 3.38 0.50 9.000000
## 5 29.0 60 0.99680 3.39 0.53 9.400000
## 6 10.0 37 0.99660 3.17 0.91 9.500000
## 7 14.0 35 0.99720 3.47 0.55 9.400000
## 8 17.0 82 0.99580 3.35 0.54 10.100000
## 9 3.0 15 0.99750 3.42 0.60 10.800000
## 10 12.0 96 0.99540 3.32 0.58 9.200000
## 11 6.0 14 0.99550 3.39 0.64 9.400000
## 12 13.0 54 0.99660 3.39 0.57 9.800000
## 13 17.0 96 0.99620 3.32 0.48 9.500000
## 14 18.0 94 0.99610 3.31 0.48 9.500000
## 15 9.0 42 0.99860 3.54 0.66 10.500000
## 16 16.0 51 0.99690 3.16 1.14 9.400000
## 17 18.0 61 0.99590 3.44 0.78 10.300000
## 18 19.0 40 0.99610 3.41 0.59 10.100000
## 19 34.0 125 0.99780 3.14 1.22 9.500000
## 20 42.0 140 0.99640 3.23 0.54 9.500000
## 21 11.0 50 0.99720 3.40 0.61 10.200000
## 22 13.0 35 0.99720 3.30 0.59 9.000000
## 23 5.0 36 0.99600 3.33 0.48 9.400000
## 24 12.0 65 0.99670 3.29 0.51 9.200000
## 25 15.0 64 0.99690 3.46 0.79 9.300000
## 26 37.0 153 0.99690 3.21 0.69 9.300000
## 27 22.0 89 0.99590 3.34 0.54 9.200000
## 28 3.0 12 0.99640 3.54 0.48 10.900000
## 29 13.0 29 0.99590 3.22 0.55 9.900000
## 30 10.0 52 0.99680 3.23 0.77 9.500000
## 31 17.0 108 0.99620 3.32 0.54 9.500000
## 32 32.0 141 0.99680 3.17 0.62 9.400000
## 33 12.0 30 0.99590 3.42 0.58 10.200000
## 34 29.0 129 0.99730 3.42 0.72 10.500000
## 35 29.0 129 0.99730 3.42 0.72 10.500000
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## quality
## 1 0
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## 5 1
## 6 0
## 7 0
## 8 0
## 9 1
## 10 0
## 11 1
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 1
## 18 0
## 19 0
## 20 0
## 21 1
## 22 1
## 23 0
## 24 0
## 25 0
## 26 0
## 27 1
## 28 0
## 29 1
## 30 0
## 31 0
## 32 0
## 33 1
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## 35 0
## 36 0
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## 342 0
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## 365 0
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## 380 1
## 381 1
## 382 0
## 383 1
## 384 0
## 385 1
## 386 1
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## 398 0
## 399 1
## 400 1df.lda <- lda(formula = train_raw.df$quality ~ ., data = train_raw.df)
df.lda## Call:
## lda(train_raw.df$quality ~ ., data = train_raw.df)
##
## Prior probabilities of groups:
## 0 1
## 0.4612177 0.5387823
##
## Group means:
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## 0 8.161121 0.5904069 0.2321338 2.574774 0.09309403
## 1 8.504334 0.4721749 0.2998762 2.535913 0.08130650
## `free sulfur dioxide` `total sulfur dioxide` density pH sulphates
## 0 16.76854 54.01808 0.9971318 3.311374 0.6218445
## 1 14.79025 38.49536 0.9964796 3.308607 0.6922910
## alcohol
## 0 9.920253
## 1 10.825413
##
## Coefficients of linear discriminants:
## LD1
## `fixed acidity` 0.16230322
## `volatile acidity` -2.62462584
## `citric acid` -0.87406767
## `residual sugar` 0.05912042
## chlorides -4.17004819
## `free sulfur dioxide` 0.01265074
## `total sulfur dioxide` -0.01312761
## density -82.63310228
## pH -0.11863955
## sulphates 2.13794530
## alcohol 0.61999292train_raw.df$quality## [1] 0 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 1 0
## [38] 0 1 1 0 1 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 0 1 1 1 0 1 1
## [75] 1 1 0 1 0 0 1 0 1 1 1 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 0 1 0 1
## [112] 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0
## [149] 1 1 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0
## [186] 1 1 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 1 1 1 1 1 1 0 1
## [223] 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 1 0 0 0 0 1 1 1 0 0
## [260] 1 0 1 0 1 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 1 1 0 1 1 1 0 1 0
## [297] 0 1 1 1 1 1 0 1 0 1 0 1 1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 1
## [334] 0 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1
## [371] 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 0
## [408] 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1
## [445] 0 1 1 0 1 0 0 0 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0
## [482] 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 0 1 0 1 0 1 0
## [519] 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 0 0 1 0
## [556] 1 0 0 1 0 0 1 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 0 1 1 1
## [593] 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 1 0 1 0
## [630] 1 1 1 1 0 0 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 0 1
## [667] 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 0 0 1 1 0 1 0 1
## [704] 0 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 1 1
## [741] 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 1 0
## [778] 0 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 0
## [815] 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0
## [852] 0 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0 0 0 1 1 0 0 0 0 0
## [889] 0 1 0 1 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 0 1 1
## [926] 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0
## [963] 1 1 0 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1 0 1 1 1 0
## [1000] 0 1 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 1 1 0 1 1 1 0 0 1 1 1 1 0 0
## [1037] 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1
## [1074] 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 1
## [1111] 1 1 0 1 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 1 0 0 0 1 0
## [1148] 1 0 1 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 1 1
## [1185] 0 0 1 0 0 1 1 1 0 0 1 1 1 0 0summary(df.lda)## Length Class Mode
## prior 2 -none- numeric
## counts 2 -none- numeric
## means 22 -none- numeric
## scaling 11 -none- numeric
## lev 2 -none- character
## svd 1 -none- numeric
## N 1 -none- numeric
## call 3 -none- call
## terms 3 terms call
## xlevels 0 -none- listprint(df.lda)## Call:
## lda(train_raw.df$quality ~ ., data = train_raw.df)
##
## Prior probabilities of groups:
## 0 1
## 0.4612177 0.5387823
##
## Group means:
## `fixed acidity` `volatile acidity` `citric acid` `residual sugar` chlorides
## 0 8.161121 0.5904069 0.2321338 2.574774 0.09309403
## 1 8.504334 0.4721749 0.2998762 2.535913 0.08130650
## `free sulfur dioxide` `total sulfur dioxide` density pH sulphates
## 0 16.76854 54.01808 0.9971318 3.311374 0.6218445
## 1 14.79025 38.49536 0.9964796 3.308607 0.6922910
## alcohol
## 0 9.920253
## 1 10.825413
##
## Coefficients of linear discriminants:
## LD1
## `fixed acidity` 0.16230322
## `volatile acidity` -2.62462584
## `citric acid` -0.87406767
## `residual sugar` 0.05912042
## chlorides -4.17004819
## `free sulfur dioxide` 0.01265074
## `total sulfur dioxide` -0.01312761
## density -82.63310228
## pH -0.11863955
## sulphates 2.13794530
## alcohol 0.61999292par(mar = c(5, 5, 2, 2)) # Set margin size (bottom, left, top, right)
plot(df.lda)
df.lda.predict <- predict(df.lda, newdata = test_raw.df)
df.lda.predict$class## [1] 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## [38] 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 0 0
## [75] 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0
## [112] 0 0 1 1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1
## [149] 1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 0
## [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 0 1 0 0 0 1 0 0 1
## [223] 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 1 1 1 1 1
## [260] 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
## [297] 1 1 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1
## [334] 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 1 1 0 1 0 1 0 1 0 0 1 0
## [371] 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 0 1 1
## Levels: 0 1df.lda.predict$x## LD1
## 1 -1.17042367
## 2 -0.42121013
## 3 -1.12074866
## 4 -1.22073170
## 5 -0.22743493
## 6 0.14095002
## 7 -1.25404936
## 8 -1.33277039
## 9 0.10986563
## 10 -1.49500908
## 11 -0.24027120
## 12 -1.29010491
## 13 -2.02431399
## 14 -2.01485406
## 15 -0.14859581
## 16 -1.55014312
## 17 0.18259908
## 18 -0.34812041
## 19 0.01306484
## 20 -1.73759381
## 21 -0.66618984
## 22 -0.97097643
## 23 -0.97161214
## 24 -1.47135676
## 25 -1.39726362
## 26 -2.87747978
## 27 -1.99473224
## 28 -1.73235451
## 29 -1.51978648
## 30 -0.92685955
## 31 -1.69038840
## 32 -2.57321406
## 33 -0.03400636
## 34 -0.63285109
## 35 -0.63285109
## 36 -1.45177654
## 37 -1.11976382
## 38 0.51338167
## 39 -0.62318990
## 40 -0.25168272
## 41 -1.15676578
## 42 -1.80048639
## 43 -1.67975461
## 44 -2.15029156
## 45 -0.39475404
## 46 -0.60326130
## 47 -0.60326130
## 48 1.45185535
## 49 -1.04104166
## 50 -0.93200181
## 51 -1.97293145
## 52 -0.66631685
## 53 -0.63179523
## 54 -0.45268085
## 55 -0.78131654
## 56 -0.14384024
## 57 -0.15508271
## 58 -1.27293257
## 59 -1.50078743
## 60 1.39621332
## 61 -1.36292526
## 62 -1.58458260
## 63 -0.63990425
## 64 -2.20641591
## 65 -1.86132262
## 66 0.30795448
## 67 0.33164485
## 68 0.55946458
## 69 0.02266293
## 70 0.55946458
## 71 0.41993974
## 72 0.11777655
## 73 -0.93393000
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## 75 -2.44332318
## 76 -1.72718442
## 77 0.54004356
## 78 0.32779725
## 79 -0.68014603
## 80 0.98889808
## 81 0.98604048
## 82 1.36195732
## 83 1.15577530
## 84 2.35944488
## 85 0.30626915
## 86 0.21614574
## 87 1.49037046
## 88 1.04977674
## 89 0.05403163
## 90 -1.58184641
## 91 1.87953165
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## 93 0.37647439
## 94 -0.65475213
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## 96 -0.08945269
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## 98 0.15590351
## 99 -0.89180457
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## 102 1.04504401
## 103 0.44434233
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## 108 -0.56875202
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## 111 -0.68208374
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## 113 -1.05842829
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## 115 0.74827066
## 116 -1.40136276
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## 119 0.47087021
## 120 0.58597793
## 121 -0.53979970
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## 124 -1.03244477
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## 126 1.55766427
## 127 2.09852409
## 128 2.07644492
## 129 -0.44633424
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## 131 1.10159531
## 132 -1.60302269
## 133 2.00157772
## 134 0.18828046
## 135 1.05962143
## 136 1.19496026
## 137 2.79949324
## 138 -0.14505701
## 139 1.91110432
## 140 0.36702749
## 141 0.79371807
## 142 -1.12372326
## 143 -1.40287997
## 144 1.06574212
## 145 0.84061932
## 146 -1.68554917
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## 149 0.82561101
## 150 0.90699112
## 151 0.88391318
## 152 -1.39124862
## 153 2.59513093
## 154 -0.98436488
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## 157 0.02809924
## 158 -0.19588765
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## 161 0.48549272
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## 165 0.10429670
## 166 0.32015048
## 167 -2.87587203
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## 170 4.47270489
## 171 1.25974145
## 172 -0.42436350
## 173 1.29710801
## 174 0.20616486
## 175 -3.23211966
## 176 -1.35541913
## 177 -0.77245031
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## 179 0.98437483
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## 183 0.41790799
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## 194 -0.86069465
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## 201 -1.34996472
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## 205 1.37631971
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## 208 1.02561949
## 209 -0.54875829
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## 219 1.10712999
## 220 -1.49852623
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## 222 1.21198171
## 223 1.00938394
## 224 1.60834719
## 225 1.92289904
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## 227 1.75243682
## 228 -0.49919621
## 229 0.16688547
## 230 -1.40978701
## 231 0.15178936
## 232 2.25988580
## 233 0.70404777
## 234 1.77806295
## 235 -1.15878674
## 236 1.67898484
## 237 0.37651829
## 238 0.17661914
## 239 1.45656060
## 240 1.45758377
## 241 0.79116885
## 242 2.35228654
## 243 -0.12647893
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## 245 1.08754412
## 246 0.53844327
## 247 -0.64035165
## 248 -0.54461661
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## 250 -0.84769727
## 251 2.07171966
## 252 -0.32514904
## 253 -0.87405211
## 254 -1.24846464
## 255 1.32125297
## 256 2.09702563
## 257 0.07844175
## 258 0.26490531
## 259 0.04962754
## 260 -1.28982879
## 261 -0.17880448
## 262 0.15351976
## 263 2.53991646
## 264 0.98364108
## 265 1.06778201
## 266 1.04462829
## 267 1.86317631
## 268 -0.14239955
## 269 1.81504682
## 270 1.11806016
## 271 0.79466427
## 272 -0.93084695
## 273 1.66156971
## 274 0.52790548
## 275 1.09972686
## 276 -0.41398984
## 277 2.37231445
## 278 1.38828748
## 279 0.96172704
## 280 0.32492543
## 281 1.51317623
## 282 2.32756929
## 283 1.34233586
## 284 2.64610431
## 285 0.44945800
## 286 1.55258383
## 287 1.25440560
## 288 1.46071260
## 289 0.02769095
## 290 0.67730531
## 291 1.37004546
## 292 -0.54955528
## 293 0.36650305
## 294 2.40250250
## 295 1.23890266
## 296 1.10323246
## 297 1.21809329
## 298 0.71696529
## 299 -1.05100583
## 300 -1.55881556
## 301 1.11930675
## 302 0.01139911
## 303 1.29456288
## 304 1.97713261
## 305 0.69706705
## 306 1.41214692
## 307 -1.26285527
## 308 -0.33858886
## 309 -0.73813818
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## 311 1.11451072
## 312 0.85375981
## 313 0.07129892
## 314 -0.64599578
## 315 0.22706851
## 316 0.29084730
## 317 -1.62650449
## 318 2.63065088
## 319 0.58735308
## 320 0.36393121
## 321 1.79460299
## 322 1.76699055
## 323 0.12397439
## 324 0.50487084
## 325 1.42675314
## 326 0.11595901
## 327 -1.19063397
## 328 1.81928197
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## 331 0.25052557
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## 336 -0.94011433
## 337 -0.43564685
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## 341 -1.33495802
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## 343 -2.16853167
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## 345 0.38572798
## 346 -2.09585425
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## 381 -0.35634340
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## 395 0.52280474
## 396 1.68330975
## 397 1.60448604
## 398 -1.72457627
## 399 0.03113497
## 400 0.73800985# Get the posteriors as a dataframe.
df.predict.posteriors <- as.data.frame(df.lda.predict$posterior)
df.predict.posteriors## 0 1
## 1 0.785649266 0.21435073
## 2 0.580807508 0.41919249
## 3 0.774587208 0.22541279
## 4 0.796444496 0.20355550
## 5 0.518612397 0.48138760
## 6 0.400389804 0.59961020
## 7 0.803368115 0.19663188
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## 9 0.410117413 0.58988259
## 10 0.848174044 0.15182596
## 11 0.522772012 0.47722799
## 12 0.810658646 0.18934135
## 13 0.917406590 0.08259341
## 14 0.916471091 0.08352891
## 15 0.493029516 0.50697048
## 16 0.857164862 0.14283514
## 17 0.387479392 0.61252061
## 18 0.557540614 0.44245939
## 19 0.440831005 0.55916900
## 20 0.884457126 0.11554287
## 21 0.655697799 0.34430220
## 22 0.738835897 0.26116410
## 23 0.738995139 0.26100486
## 24 0.844176802 0.15582320
## 25 0.831098774 0.16890123
## 26 0.971122900 0.02887710
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## 35 0.645860339 0.35413966
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## 353 0.056596621 0.94340338
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## 355 0.339018482 0.66098152
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## 372 0.230344665 0.76965534
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## 379 0.492590015 0.50740998
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## 381 0.560172881 0.43982712
## 382 0.753009765 0.24699023
## 383 0.497652449 0.50234755
## 384 0.409544132 0.59045587
## 385 0.246939554 0.75306045
## 386 0.091189541 0.90881046
## 387 0.364820116 0.63517988
## 388 0.103915733 0.89608427
## 389 0.786637622 0.21336238
## 390 0.921063723 0.07893628
## 391 0.617863092 0.38213691
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## 393 0.097765153 0.90223485
## 394 0.910804132 0.08919587
## 395 0.289121186 0.71087881
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## 397 0.090779625 0.90922037
## 398 0.882718559 0.11728144
## 399 0.435055693 0.56494431
## 400 0.235216461 0.76478354pred <- prediction(df.predict.posteriors[,2], test_raw.df$quality)
pred## A prediction instance
## with 400 data pointscolnames(df.predict.posteriors)## [1] "0" "1"str(df.predict.posteriors)## 'data.frame': 400 obs. of 2 variables:
## $ 0: num 0.786 0.581 0.775 0.796 0.519 ...
## $ 1: num 0.214 0.419 0.225 0.204 0.481 ...head(df.predict.posteriors)## 0 1
## 1 0.7856493 0.2143507
## 2 0.5808075 0.4191925
## 3 0.7745872 0.2254128
## 4 0.7964445 0.2035555
## 5 0.5186124 0.4813876
## 6 0.4003898 0.5996102- Model Insights LDA
roc.perf = performance(pred, measure = "tpr", x.measure = "fpr")
auc.train <- performance(pred, measure = "auc")
auc.train <- auc.train@y.values
plot(roc.perf)
abline(a=0, b= 1)
text(x = .25, y = .65 ,paste("AUC = ", round(auc.train[[1]],3), sep = ""))
# Read the CSV file
df <- read_csv("C:/Rutgers/Subjects/Spring Sem/Multivariate Analysis/Data/wine_new.csv")## Rows: 1143 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): quality
## dbl (11): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Select specific columns by names
df <- df[c("alcohol","volatile acidity","quality")]
df## # A tibble: 1,143 × 3
## alcohol `volatile acidity` quality
## <dbl> <dbl> <chr>
## 1 9.4 0.7 Average
## 2 9.8 0.88 Average
## 3 9.8 0.76 Average
## 4 9.8 0.28 Good
## 5 9.4 0.7 Average
## 6 9.4 0.66 Average
## 7 9.4 0.6 Average
## 8 10 0.65 Very Good
## 9 9.5 0.58 Very Good
## 10 9.2 0.58 Average
## # ℹ 1,133 more rows- Model Insights (10 points): LDA
Prior Probabilities: These represent the class proportions in the dataset. Notably, “Average” and “Good” classes dominate, while “Bad” is rare.
Group Means: Average alcohol and volatile acidity levels vary across quality classes.
Coefficients of Linear Discriminants: Alcohol positively influences LD1, while volatile acidity negatively affects both LD1 and LD2.
Proportion of Trace: LD1 explains most variance (95%), indicating its importance in class separation.
In summary, alcohol and volatile acidity significantly influence quality class discrimination, with alcohol predominantly shaping LD1, while both variables contribute to LD2.
r <- lda(formula = quality ~ ., data = df)
r## Call:
## lda(quality ~ ., data = df)
##
## Prior probabilities of groups:
## Average Bad Excellent Good Poor Very Good
## 0.422572178 0.005249344 0.013998250 0.404199475 0.028871391 0.125109361
##
## Group means:
## alcohol `volatile acidity`
## Average 9.902277 0.5852795
## Bad 9.691667 0.8975000
## Excellent 11.937500 0.4100000
## Good 10.655339 0.5049567
## Poor 10.260606 0.7000000
## Very Good 11.482634 0.3936713
##
## Coefficients of linear discriminants:
## LD1 LD2
## alcohol 0.8636293 -0.6433275
## `volatile acidity` -3.5787372 -4.9558022
##
## Proportion of trace:
## LD1 LD2
## 0.949 0.051- Model Acceptance
Prior Probabilities of Groups: The prior probabilities indicate the proportion of each quality class in the dataset. “Average” and “Good” classes are most prevalent, while “Bad” is rarest.
Group Means: Average alcohol and volatile acidity levels differ across quality classes. For instance, “Excellent” quality wines have the highest alcohol content on average.
Coefficients of Linear Discriminants: The coefficients show the weights assigned to each predictor variable (alcohol and volatile acidity) in the linear combination used for classification.
Higher alcohol content and lower volatile acidity contribute positively to LD1.
Proportion of Trace: LD1 explains approximately 95% of the total variance, suggesting its significance in class separation. LD2 explains the remaining 5%.
summary(r)## Length Class Mode
## prior 6 -none- numeric
## counts 6 -none- numeric
## means 12 -none- numeric
## scaling 4 -none- numeric
## lev 6 -none- character
## svd 2 -none- numeric
## N 1 -none- numeric
## call 3 -none- call
## terms 3 terms call
## xlevels 0 -none- listprint(r)## Call:
## lda(quality ~ ., data = df)
##
## Prior probabilities of groups:
## Average Bad Excellent Good Poor Very Good
## 0.422572178 0.005249344 0.013998250 0.404199475 0.028871391 0.125109361
##
## Group means:
## alcohol `volatile acidity`
## Average 9.902277 0.5852795
## Bad 9.691667 0.8975000
## Excellent 11.937500 0.4100000
## Good 10.655339 0.5049567
## Poor 10.260606 0.7000000
## Very Good 11.482634 0.3936713
##
## Coefficients of linear discriminants:
## LD1 LD2
## alcohol 0.8636293 -0.6433275
## `volatile acidity` -3.5787372 -4.9558022
##
## Proportion of trace:
## LD1 LD2
## 0.949 0.051r$counts## Average Bad Excellent Good Poor Very Good
## 483 6 16 462 33 143r$means## alcohol `volatile acidity`
## Average 9.902277 0.5852795
## Bad 9.691667 0.8975000
## Excellent 11.937500 0.4100000
## Good 10.655339 0.5049567
## Poor 10.260606 0.7000000
## Very Good 11.482634 0.3936713r$scaling## LD1 LD2
## alcohol 0.8636293 -0.6433275
## `volatile acidity` -3.5787372 -4.9558022r$prior## Average Bad Excellent Good Poor Very Good
## 0.422572178 0.005249344 0.013998250 0.404199475 0.028871391 0.125109361r$lev## [1] "Average" "Bad" "Excellent" "Good" "Poor" "Very Good"r$svd## [1] 11.066348 2.565922#singular values (svd) that gives the ratio of the between- and within-group standard deviations on the linear discriminant variables.
r$N## [1] 1143r$call## lda(formula = quality ~ ., data = df)(prop = r$svd^2/sum(r$svd^2))## [1] 0.94898058 0.05101942#we can use the singular values to compute the amount of the between-group variance that is explained by each linear discriminant. In our example we see that the first linear discriminant explains more than 99% of the between-group variance in the iris dataset.
r2 <- lda(formula = quality ~ ., data = df, CV = TRUE)
r2## $class
## [1] Average Average Average Good Average Average Average
## [8] Average Average Average Average Average Good Average
## [15] Average Average Average Average Average Average Average
## [22] Average Average Average Average Average Good Average
## [29] Good Average Good Average Very Good Average Average
## [36] Average Average Average Average Average Average Average
## [43] Average Average Average Average Average Average Average
## [50] Average Average Good Average Average Average Average
## [57] Average Good Average Average Average Average Average
## [64] Average Average Average Average Average Average Average
## [71] Average Average Average Average Average Average Average
## [78] Average Average Good Average Good Average Average
## [85] Good Average Average Bad Bad Good Average
## [92] Very Good Average Average Average Average Very Good Average
## [99] Very Good Average Average Good Good Average Average
## [106] Average Good Good Good Average Average Average
## [113] Average Average Average Average Average Average Average
## [120] Average Average Average Good Average Average Good
## [127] Average Average Average Average Average Average Average
## [134] Good Average Average Average Good Average Average
## [141] Average Good Average Good Average Good Good
## [148] Average Good Average Average Average Good Good
## [155] Average Average Average Average Average Average Good
## [162] Average Good Good Average Average Average Average
## [169] Good Average Average Average Average Average Average
## [176] Good Average Good Average Average Average Average
## [183] Average Good Average Average Average Average Good
## [190] Average Very Good Average Good Good Good Average
## [197] Good Very Good Good Average Good Good Average
## [204] Average Good Good Good Average Good Average
## [211] Good Average Average Average Average Average Good
## [218] Good Average Average Average Average Average Average
## [225] Average Average Good Good Average Average Average
## [232] Average Good Good Good Good Good Good
## [239] Very Good Good Good Average Average Good Good
## [246] Average Average Average Very Good Very Good Good Good
## [253] Good Good Good Average Average Average Good
## [260] Average Average Average Very Good Average Good Good
## [267] Very Good Very Good Good Average Average Good Good
## [274] Average Very Good Average Good Average Average Good
## [281] Average Average Good Good Average Average Average
## [288] Good Average Average Good Good Good Average
## [295] Good Average Good Good Good Average Average
## [302] Average Good Average Average Good Average Average
## [309] Good Average Good Good Good Good Good
## [316] Average Good Good Average Good Good Very Good
## [323] Average Average Average Good Average Average Good
## [330] Very Good Average Average Good Good Good Average
## [337] Good Good Average Average Very Good Average Average
## [344] Average Good Very Good Very Good Good Very Good Good
## [351] Average Good Good Good Average Good Good
## [358] Good Very Good Good Average Good Average Average
## [365] Good Good Average Good Average Good Average
## [372] Average Good Average Good Good Good Very Good
## [379] Average Good Average Average Very Good Good Average
## [386] Good Average Good Average Average Average Good
## [393] Good Average Good Average Average Good Good
## [400] Good Very Good Good Average Good Very Good Good
## [407] Average Good Good Good Good Average Average
## [414] Average Good Average Average Good Average Very Good
## [421] Average Good Average Average Average Average Average
## [428] Average Average Good Average Very Good Average Average
## [435] Average Average Good Average Average Average Average
## [442] Average Average Average Average Average Average Good
## [449] Good Average Average Average Average Average Good
## [456] Average Average Average Average Good Good Average
## [463] Very Good Very Good Average Average Average Good Average
## [470] Average Average Average Average Average Average Average
## [477] Average Average Average Average Average Good Good
## [484] Average Good Average Good Average Average Average
## [491] Average Average Average Very Good Average Average Good
## [498] Average Average Average Average Average Average Average
## [505] Good Good Average Average Average Average Average
## [512] Average Average Average Average Average Good Average
## [519] Average Good Average Good Average Average Average
## [526] Average Average Average Good Average Average Average
## [533] Average Average Average Average Average Average Average
## [540] Average Average Average Average Average Average Average
## [547] Average Average Average Average Average Average Average
## [554] Average Average Average Average Average Good Average
## [561] Average Average Average Average Average Average Average
## [568] Average Average Average Good Very Good Average Average
## [575] Good Good Average Good Average Average Very Good
## [582] Average Good Good Good Good Good Average
## [589] Average Very Good Average Good Good Good Good
## [596] Good Average Very Good Very Good Good Average Good
## [603] Average Average Average Average Average Average Good
## [610] Good Good Good Good Average Good Average
## [617] Average Good Good Very Good Good Good Very Good
## [624] Average Average Average Good Average Average Average
## [631] Average Average Good Very Good Good Very Good Very Good
## [638] Good Good Good Good Good Good Very Good
## [645] Good Good Very Good Good Good Good Good
## [652] Good Good Good Good Good Average Good
## [659] Very Good Average Average Average Very Good Good Very Good
## [666] Good Very Good Good Very Good Very Good Very Good Very Good
## [673] Good Very Good Good Good Good Good Average
## [680] Average Good Average Very Good Good Very Good Average
## [687] Average Good Good Average Very Good Good Good
## [694] Average Average Good Average Average Average Average
## [701] Average Good Good Good Very Good Very Good Very Good
## [708] Very Good Very Good Good Average Average Good Very Good
## [715] Very Good Good Good Good Good Very Good Good
## [722] Average Very Good Good Good Average Average Good
## [729] Very Good Very Good Good Average Very Good Good Average
## [736] Good Good Good Average Good Very Good Average
## [743] Good Average Good Good Good Very Good Good
## [750] Very Good Good Average Good Good Good Good
## [757] Average Good Good Good Very Good Very Good Average
## [764] Good Average Good Very Good Good Good Very Good
## [771] Good Good Very Good Average Average Average Very Good
## [778] Good Good Good Good Very Good Average Good
## [785] Average Very Good Good Very Good Good Good Good
## [792] Very Good Good Very Good Good Good Good Good
## [799] Average Very Good Good Very Good Very Good Good Average
## [806] Average Average Good Good Good Good Good
## [813] Average Good Good Very Good Good Average Good
## [820] Average Very Good Very Good Very Good Average Very Good Very Good
## [827] Good Good Very Good Average Average Good Average
## [834] Average Good Good Average Good Good Average
## [841] Very Good Average Very Good Average Average Average Good
## [848] Average Average Good Good Average Good Good
## [855] Average Good Good Average Average Good Average
## [862] Good Good Good Good Average Average Average
## [869] Very Good Average Very Good Average Average Good Average
## [876] Good Average Good Average Average Good Average
## [883] Good Average Average Average Good Average Good
## [890] Good Average Average Average Good Average Average
## [897] Good Average Very Good Good Good Average Good
## [904] Average Good Average Average Good Good Good
## [911] Average Average Good Good Average Average Good
## [918] Good Very Good Average Good Good Poor Good
## [925] Good Good Average Average Average Average Very Good
## [932] Bad Average Good Average Good Good Good
## [939] Good Good Good Average Average Average Average
## [946] Average Average Average Good Average Average Average
## [953] Good Good Good Good Good Good Good
## [960] Average Good Average Good Good Good Average
## [967] Average Average Average Good Average Good Average
## [974] Average Good Average Average Average Good Good
## [981] Average Average Average Average Average Average Very Good
## [988] Good Average Average Average Average Average Good
## [995] Average Very Good Good Good Good Good Very Good
## [1002] Good Good Good Good Average Good Average
## [1009] Good Very Good Average Average Average Average Good
## [1016] Good Good Good Very Good Good Average Good
## [1023] Average Average Average Average Good Good Average
## [1030] Average Good Good Good Good Good Average
## [1037] Average Good Very Good Good Average Good Average
## [1044] Average Average Average Average Average Good Good
## [1051] Good Average Very Good Average Very Good Average Good
## [1058] Average Good Good Average Average Average Good
## [1065] Very Good Good Average Good Good Good Good
## [1072] Average Good Good Average Average Good Good
## [1079] Good Average Average Average Average Good Good
## [1086] Average Good Good Average Average Good Average
## [1093] Good Average Average Average Good Average Good
## [1100] Good Good Average Average Good Good Average
## [1107] Good Average Good Average Average Average Average
## [1114] Average Average Average Average Good Very Good Average
## [1121] Average Good Very Good Good Average Good Good
## [1128] Good Good Good Very Good Good Average Good
## [1135] Good Good Good Good Good Average Average
## [1142] Good Average
## Levels: Average Bad Excellent Good Poor Very Good
##
## $posterior
## Average Bad Excellent Good Poor
## 1 0.7324357553 0.00612866251102 0.000191139955 0.208448533 0.04802675153
## 2 0.6776193688 0.04269061074270 0.000143951501 0.161424710 0.11553985051
## 3 0.6833590715 0.01056211282723 0.000311337847 0.230791088 0.06893412082
## 4 0.3785986912 0.00002171806241 0.003621123332 0.516629986 0.00477379777
## 5 0.7324357553 0.00612866251102 0.000191139955 0.208448533 0.04802675153
## 6 0.7198502926 0.00377938127196 0.000242232905 0.230237610 0.03968659675
## 7 0.6944912825 0.00181332675769 0.000342149703 0.264705237 0.02953505138
## 8 0.6055372886 0.00247819445578 0.000873429402 0.333984715 0.04125412689
## 9 0.6670316847 0.00134867097523 0.000462513336 0.292602680 0.02713732690
## 10 0.7199942099 0.00155550704984 0.000252056252 0.244834265 0.02580114559
## 11 0.6055012025 0.00169068899527 0.000874791116 0.340241621 0.03398447943
## 12 0.7489174479 0.00234481162573 0.000171349936 0.213963293 0.02928656916
## 13 0.2234893340 0.00001111991435 0.011001045232 0.561923589 0.00380980869
## 14 0.5563132213 0.00005788953007 0.001006387345 0.400266344 0.00645024729
## 15 0.4906068491 0.00010406649340 0.001857330408 0.449429629 0.00958399962
## 16 0.5681677421 0.00020059922250 0.001043277609 0.389025812 0.01210946596
## 17 0.6317821138 0.00045850803625 0.000626362605 0.332493091 0.01671327240
## 18 0.5002953597 0.00011930092072 0.001766478611 0.441844078 0.01017858842
## 19 0.5832657816 0.00013480462225 0.000865414218 0.378236815 0.00960804308
## 20 0.5531016248 0.00015465498856 0.001153813016 0.401771185 0.01081479911
## 21 0.7349636636 0.00691030265134 0.000179984855 0.203154007 0.05033044136
## 22 0.6427251831 0.00259715499887 0.000604487714 0.302062454 0.03934863409
## 23 0.5971345552 0.00321307403594 0.000942183669 0.334888221 0.04732546175
## 24 0.6466694954 0.00293858594334 0.000573554592 0.296542692 0.04135161622
## 25 0.5520983668 0.00005750935913 0.001013978051 0.404298122 0.00644262358
## 26 0.6805291949 0.00287103751373 0.000399836835 0.268626981 0.03825829864
## 27 0.3943817080 0.00076281786515 0.005153035458 0.503802889 0.03044914737
## 28 0.4846747506 0.00009170924977 0.001929303334 0.454772189 0.00904819804
## 29 0.3493166887 0.00012571391407 0.005870796068 0.535830698 0.01244117568
## 30 0.7176565848 0.00214741141546 0.000262101805 0.243435840 0.02918103715
## 31 0.3810053298 0.00021853351633 0.004957114502 0.518429303 0.01609769736
## 32 0.5467913834 0.00236818941890 0.001515872869 0.380970908 0.04423859918
## 33 0.0264256907 0.00001196330652 0.131484317097 0.320112302 0.00276668323
## 34 0.7007490373 0.09616261979221 0.000025461931 0.084462228 0.11799467325
## 35 0.7510692605 0.00411872203933 0.000158095474 0.202107686 0.03804392166
## 36 0.6519974930 0.00067076672748 0.000530724119 0.312271733 0.01961007639
## 37 0.6395986012 0.00052135621473 0.000591241628 0.324806238 0.01764345210
## 38 0.5529852407 0.00011130752089 0.001105448435 0.402687233 0.00910447655
## 39 0.4669296025 0.00036035165377 0.002650053580 0.462098138 0.01892534297
## 40 0.5377528867 0.00016537784702 0.001330087829 0.413449149 0.01145555179
## 41 0.5689413049 0.00116252241918 0.001217343968 0.375008695 0.02991237861
## 42 0.5631053380 0.00012735187684 0.001046575290 0.394034576 0.00964653734
## 43 0.6125413306 0.00016255329013 0.000674555580 0.353751932 0.01009590884
## 44 0.7448227559 0.00566863205366 0.000163811063 0.200181293 0.04482531491
## 45 0.6773651107 0.00781978612957 0.000361672393 0.246031932 0.06128628110
## 46 0.4525841344 0.00369082007834 0.003399784000 0.439707980 0.06288718468
## 47 0.4525841344 0.00369082007834 0.003399784000 0.439707980 0.06288718468
## 48 0.4853033600 0.00326609889015 0.002586001392 0.419827251 0.05669049141
## 49 0.5302446357 0.00370864036088 0.001758946079 0.383141843 0.05670367579
## 50 0.7137782695 0.00520141139692 0.000250965960 0.228033929 0.04675798678
## 51 0.7570577085 0.00493397550167 0.000144559253 0.194650828 0.03913344957
## 52 0.3180212418 0.00007177533761 0.006900816500 0.549143814 0.00953488342
## 53 0.5264088102 0.00896997915615 0.001675202747 0.356363159 0.08671949990
## 54 0.6285824138 0.00810993946083 0.000616726261 0.283410767 0.06900141672
## 55 0.7561034279 0.00281162146590 0.000156871293 0.206373655 0.02973078366
## 56 0.6467859693 0.00030752552417 0.000517819013 0.322245802 0.01322602261
## 57 0.6966288467 0.00060601719347 0.000320893159 0.274806077 0.01693948693
## 58 0.2792028495 0.00001820068130 0.007600104471 0.555360329 0.00475784502
## 59 0.5194391109 0.00065233053757 0.001810515766 0.419711574 0.02398241962
## 60 0.5221239494 0.00034042836526 0.001665153289 0.422194626 0.01707914574
## 61 0.5891683138 0.00040958250985 0.000932950981 0.368502301 0.01696975445
## 62 0.5376563899 0.00011904380848 0.001274356859 0.414371810 0.00964449556
## 63 0.7674997227 0.00275945425827 0.000130304559 0.195332782 0.03004175127
## 64 0.6303903548 0.00063491521798 0.000652654561 0.331147597 0.01980367948
## 65 0.5221239494 0.00034042836526 0.001665153289 0.422194626 0.01707914574
## 66 0.5891683138 0.00040958250985 0.000932950981 0.368502301 0.01696975445
## 67 0.5154562214 0.14265453637215 0.000230306917 0.148237068 0.19082261708
## 68 0.5197461750 0.00792186739706 0.001779814761 0.366956281 0.08235600601
## 69 0.6692798000 0.05333995066780 0.000125162981 0.150425830 0.12461416566
## 70 0.7392942683 0.00110893472903 0.000199753998 0.231682396 0.02087542429
## 71 0.5382502086 0.00132879784983 0.001618157191 0.397158626 0.03359526374
## 72 0.7392942683 0.00110893472903 0.000199753998 0.231682396 0.02087542429
## 73 0.7176608296 0.00146280569914 0.000259355024 0.247641772 0.02516797171
## 74 0.6317821138 0.00045850803625 0.000626362605 0.332493091 0.01671327240
## 75 0.7176608296 0.00146280569914 0.000259355024 0.247641772 0.02516797171
## 76 0.5979771414 0.00017453915254 0.000780910340 0.365492720 0.01073770117
## 77 0.7248292152 0.00274235098548 0.000233448833 0.232127920 0.03363784652
## 78 0.6545758117 0.00105010547965 0.000525552752 0.305784862 0.02443678692
## 79 0.6287149590 0.00190091008401 0.000696256394 0.319323412 0.03455423922
## 80 0.4275884842 0.00003561224200 0.002672627734 0.490292175 0.00588564317
## 81 0.6545758117 0.00105010547965 0.000525552752 0.305784862 0.02443678692
## 82 0.3113528706 0.00006258595164 0.007172065028 0.550818994 0.00891536192
## 83 0.5354447377 0.00061133447436 0.001571621072 0.407983554 0.02268444941
## 84 0.4848109026 0.34532759746852 0.000004577069 0.033020479 0.13672548626
## 85 0.3351120416 0.00035471167174 0.007600644637 0.540557881 0.02175785838
## 86 0.7604578292 0.00627048746910 0.000128116079 0.184745916 0.04483141466
## 87 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 88 0.0695460471 0.77200626154342 0.000011140302 0.011685644 0.14666950608
## 89 0.0695460471 0.77200626154342 0.000011140302 0.011685644 0.14666950608
## 90 0.4565068318 0.00083477938209 0.003137657822 0.463604008 0.02969378934
## 91 0.7416134200 0.01048939040623 0.000145366309 0.185100459 0.05912934457
## 92 0.0269550729 0.00001024167922 0.123603975264 0.325192824 0.00356853179
## 93 0.5211512365 0.21140256029823 0.000048109152 0.080757988 0.18590212229
## 94 0.7121434266 0.00964497695492 0.000223219127 0.211315786 0.06180858935
## 95 0.6231258067 0.00019699368895 0.000623582071 0.344250744 0.01095685246
## 96 0.7121434266 0.00964497695492 0.000223219127 0.211315786 0.06180858935
## 97 0.0018188619 0.00000008254512 0.245588001976 0.080489850 0.00018076764
## 98 0.5606800248 0.00012683335797 0.001051471544 0.396369978 0.00963791738
## 99 0.0018188619 0.00000008254512 0.245588001976 0.080489850 0.00018076764
## 100 0.7233484291 0.00426691616831 0.000228423566 0.224698970 0.04164511327
## 101 0.6196698726 0.00361019552739 0.000749390767 0.314093947 0.04808853053
## 102 0.4516122538 0.00027617712947 0.002910073887 0.473855207 0.01678976668
## 103 0.3021711505 0.00002798788841 0.006796731865 0.550930696 0.00586583019
## 104 0.6517733112 0.00066818963114 0.000533118227 0.313740140 0.01831621344
## 105 0.5534758620 0.00124373352351 0.001404708804 0.386332466 0.03170947501
## 106 0.5534758620 0.00124373352351 0.001404708804 0.386332466 0.03170947501
## 107 0.3336470112 0.00009509492326 0.006371078022 0.542978617 0.01090274377
## 108 0.3336470112 0.00009509492326 0.006371078022 0.542978617 0.01090274377
## 109 0.3336470112 0.00009509492326 0.006371078022 0.542978617 0.01090274377
## 110 0.7420545071 0.00502627894675 0.000173918236 0.205345860 0.04276343037
## 111 0.7135372907 0.00190271389755 0.000275614403 0.247470501 0.02905716503
## 112 0.6543657368 0.09814869121644 0.000055498667 0.106864587 0.13950861738
## 113 0.7584342712 0.00523928303095 0.000140283009 0.192167172 0.04007304949
## 114 0.5283057209 0.00053696220900 0.001652384399 0.414516902 0.02143608597
## 115 0.6944912825 0.00181332675769 0.000342149703 0.264705237 0.02953505138
## 116 0.6895976804 0.00160273120774 0.000362025201 0.270645332 0.02808672942
## 117 0.6905902669 0.00250203268880 0.000355447943 0.262861728 0.03488618548
## 118 0.5565956664 0.00195057826745 0.001385600509 0.376957423 0.03956502233
## 119 0.7413009987 0.00117389106543 0.000195804600 0.231069692 0.01958983063
## 120 0.5066536853 0.00151239608328 0.002143431081 0.419160471 0.03754382504
## 121 0.7562756372 0.05822106203340 0.000029730057 0.096045748 0.08865559134
## 122 0.6291089333 0.00021068623583 0.000603502407 0.338658781 0.01126476564
## 123 0.2569156346 0.00054084099491 0.015244332552 0.566163516 0.02917477608
## 124 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 125 0.5298020975 0.00010437846526 0.001338589431 0.420601002 0.00910262335
## 126 0.4040851244 0.00032858332882 0.004345968711 0.503640066 0.01943407582
## 127 0.7174118292 0.01958845954214 0.000153542086 0.178750990 0.08087843715
## 128 0.6811985711 0.00195544087420 0.000398100086 0.274896594 0.03156081777
## 129 0.6811985711 0.00195544087420 0.000398100086 0.274896594 0.03156081777
## 130 0.7165006364 0.00214775902519 0.000262046811 0.243281960 0.03049596466
## 131 0.7533219982 0.00914107286668 0.000128453025 0.180158002 0.05393628835
## 132 0.7165006364 0.00214775902519 0.000262046811 0.243281960 0.03049596466
## 133 0.7222983981 0.00243083073787 0.000245532128 0.236134883 0.03206951513
## 134 0.4244396061 0.00003533342594 0.002682242451 0.493273711 0.00587287864
## 135 0.6053444404 0.00014294361730 0.000710571547 0.360097862 0.00955602141
## 136 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 137 0.6382253423 0.00052037999243 0.000593742519 0.326150680 0.01762766928
## 138 0.4522998670 0.00007439222634 0.002443067691 0.475492685 0.00843332266
## 139 0.6077230751 0.00083132067023 0.000830470135 0.347460043 0.02365829836
## 140 0.6077230751 0.00083132067023 0.000830470135 0.347460043 0.02365829836
## 141 0.5177097236 0.00090241258596 0.001885196901 0.417788379 0.02839465144
## 142 0.3240418448 0.00002204721702 0.005518616365 0.541775180 0.00506915709
## 143 0.3675256365 0.19270617555519 0.000874659482 0.188014076 0.24505155100
## 144 0.2947108228 0.00002439647529 0.007041203188 0.554462107 0.00548534409
## 145 0.6358425828 0.00023928743047 0.000572445624 0.332465012 0.01188985717
## 146 0.1808328580 0.00001065592904 0.015846024266 0.556003971 0.00382111699
## 147 0.1808328580 0.00001065592904 0.015846024266 0.556003971 0.00382111699
## 148 0.5976537243 0.00024224693501 0.000814699138 0.364451066 0.01274473334
## 149 0.0757556889 0.00004610138595 0.066051173848 0.473084624 0.00858184161
## 150 0.6401409631 0.00628817842095 0.000577725447 0.282352766 0.06031101067
## 151 0.5334481999 0.00084547884985 0.001637670230 0.406504612 0.02684886389
## 152 0.6791251001 0.00782563503916 0.000359094410 0.244240510 0.06135587788
## 153 0.3805861870 0.00021830609775 0.004957113502 0.518824321 0.01610299325
## 154 0.3658325465 0.00091021245270 0.006538954191 0.518790333 0.03439246872
## 155 0.7789713566 0.00931655780170 0.000085713227 0.159012022 0.05014692987
## 156 0.5670591661 0.00038584320036 0.001134760774 0.386670448 0.01704333591
## 157 0.5246052985 0.00006614252854 0.001338928486 0.424390642 0.00723265794
## 158 0.6567217863 0.00075926295231 0.000504823280 0.307295029 0.02064415613
## 159 0.6811985711 0.00195544087420 0.000398100086 0.274896594 0.03156081777
## 160 0.6025112923 0.00233232545485 0.000906540994 0.337004574 0.04016857995
## 161 0.3344748768 0.00009539007438 0.006368475692 0.542212537 0.01090445011
## 162 0.6721644626 0.00152810532721 0.000443342206 0.286022885 0.02855179858
## 163 0.0783705175 0.00002785281006 0.058201360544 0.477854776 0.00655163994
## 164 0.4362312550 0.00007893735547 0.002796252453 0.485977038 0.00886552653
## 165 0.5005126974 0.00008589558017 0.001690203936 0.442011740 0.00856587893
## 166 0.6280293228 0.19397777156558 0.000008848834 0.049764625 0.12799356900
## 167 0.7728519138 0.00311917589814 0.000121287531 0.188458685 0.03151957792
## 168 0.7267892910 0.00291460078207 0.000226757871 0.229378268 0.03446647693
## 169 0.2137935878 0.00003122112992 0.014270284819 0.566712763 0.00665852414
## 170 0.6060650469 0.00114996216145 0.000862601691 0.345049621 0.02802230458
## 171 0.4677388684 0.00001351456621 0.001672926942 0.466008392 0.00335999814
## 172 0.4677388684 0.00001351456621 0.001672926942 0.466008392 0.00335999814
## 173 0.6306438517 0.00296968806278 0.000681760394 0.309306682 0.04298748813
## 174 0.7104783809 0.00460790164711 0.000266189627 0.233699075 0.04456686746
## 175 0.6306438517 0.00296968806278 0.000681760394 0.309306682 0.04298748813
## 176 0.3634898757 0.00003193227592 0.004311793214 0.525746462 0.00594278501
## 177 0.5971818697 0.00139945799516 0.000946428599 0.349699620 0.03141343677
## 178 0.2718821475 0.00003043854043 0.008666612363 0.561200820 0.00629604341
## 179 0.7306625119 0.01435542075512 0.000149651297 0.182199147 0.06926158850
## 180 0.6976459080 0.00131283665905 0.000329012706 0.266308833 0.02498034909
## 181 0.5677908461 0.00007474354313 0.000917462343 0.391768438 0.00722348011
## 182 0.7604578292 0.00627048746910 0.000128116079 0.184745916 0.04483141466
## 183 0.5757810003 0.00016448950695 0.000950368330 0.383650620 0.01078881155
## 184 0.2445354604 0.00001726361096 0.009953373536 0.563259411 0.00474735116
## 185 0.5367887940 0.00006152802143 0.001168927916 0.415964660 0.00682554835
## 186 0.6554640272 0.13546476847098 0.000029268166 0.082761682 0.12566920506
## 187 0.5207007842 0.00047114313135 0.001737632804 0.421350033 0.02024040682
## 188 0.5681448079 0.00010395760529 0.000957866988 0.390930136 0.00858541454
## 189 0.3417333492 0.00040708990957 0.007290638358 0.537129587 0.02322943690
## 190 0.6853820793 0.03805821584564 0.000154447353 0.167198331 0.10641584381
## 191 0.0170788689 0.00000117165653 0.110590822623 0.256451018 0.00105147456
## 192 0.6638524035 0.00086193127953 0.000475798190 0.299838553 0.02176963888
## 193 0.2325889768 0.00000385797189 0.008507910621 0.550476767 0.00215514546
## 194 0.3766998587 0.00039257916919 0.005521704786 0.518754929 0.02197828853
## 195 0.3766998587 0.00039257916919 0.005521704786 0.518754929 0.02197828853
## 196 0.6638524035 0.00086193127953 0.000475798190 0.299838553 0.02176963888
## 197 0.2325889768 0.00000385797189 0.008507910621 0.550476767 0.00215514546
## 198 0.0197358244 0.00000099590933 0.097929686738 0.271993413 0.00097954938
## 199 0.3114838040 0.00006267833040 0.007167280806 0.551651677 0.00892427159
## 200 0.4843125262 0.00002440971762 0.001622600579 0.452410063 0.00451260566
## 201 0.3469807835 0.00001745486964 0.004441333621 0.533940813 0.00438019240
## 202 0.3114838040 0.00006267833040 0.007167280806 0.551651677 0.00892427159
## 203 0.6115238413 0.00130407395058 0.000819222405 0.339058594 0.02952321197
## 204 0.6115238413 0.00130407395058 0.000819222405 0.339058594 0.02952321197
## 205 0.3966459963 0.00014904609460 0.004164180381 0.510089233 0.01294629612
## 206 0.3570802076 0.00007491968080 0.005148877413 0.531202079 0.00938838301
## 207 0.3810669452 0.00030251157247 0.005174082326 0.518116220 0.01912221479
## 208 0.6265962128 0.00020998840146 0.000607373530 0.341090834 0.01125806811
## 209 0.3810669452 0.00030251157247 0.005174082326 0.518116220 0.01912221479
## 210 0.4734573332 0.00056952508675 0.002634741811 0.454491228 0.02380078084
## 211 0.3721236188 0.00005108559315 0.004328858232 0.522764362 0.00755373207
## 212 0.5006321453 0.00006179929534 0.001616620853 0.441986126 0.00720525893
## 213 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 214 0.6736143553 0.02310762599963 0.000248816070 0.202396686 0.09625900860
## 215 0.6724198051 0.00238422576953 0.000437473086 0.279039304 0.03544209112
## 216 0.6632301123 0.00290502560764 0.000480386490 0.283046940 0.03977320200
## 217 0.3654045780 0.00031983803573 0.005891057437 0.525815571 0.01998242879
## 218 0.2325889768 0.00000385797189 0.008507910621 0.550476767 0.00215514546
## 219 0.6308013326 0.00491800057264 0.000651080372 0.297285426 0.05455979021
## 220 0.7478789589 0.00365026347345 0.000167761963 0.207217611 0.03627654696
## 221 0.7037308722 0.00231907788969 0.000305331323 0.252987183 0.03263072985
## 222 0.5550280023 0.00015519137094 0.001149588719 0.399912013 0.01082405502
## 223 0.5698602468 0.00020119084049 0.001039626660 0.387387448 0.01211925186
## 224 0.6579950712 0.00076048099428 0.000502590900 0.306040708 0.02066475935
## 225 0.5913950263 0.00029616906102 0.000891825516 0.368373285 0.01432154472
## 226 0.6185019896 0.00035560071054 0.000696450358 0.345240352 0.01501022088
## 227 0.1811671995 0.00002047087151 0.017533585875 0.561379981 0.00543213936
## 228 0.2835221289 0.00094758731184 0.013004475892 0.556669473 0.03823767645
## 229 0.5803891954 0.00884511348983 0.001005127201 0.316839876 0.07880845252
## 230 0.6431458660 0.00176841740463 0.000600451931 0.308489308 0.03244137171
## 231 0.6737915978 0.00051024907610 0.000414343613 0.295730094 0.01633866290
## 232 0.6737915978 0.00051024907610 0.000414343613 0.295730094 0.01633866290
## 233 0.1993830822 0.00014689928422 0.020914525124 0.573090729 0.01533142376
## 234 0.1457507046 0.00003770646250 0.027432345153 0.551794125 0.00766085963
## 235 0.3945266330 0.00002055651486 0.003179142407 0.507901258 0.00456221890
## 236 0.3194031567 0.00037258104802 0.008618578102 0.547781317 0.02267116081
## 237 0.2374621146 0.00067766314302 0.018549417891 0.570783398 0.03341291426
## 238 0.1754634168 0.00076387206355 0.033243319935 0.569684904 0.03721678099
## 239 0.0410677163 0.00000758805493 0.085741074901 0.375184111 0.00314903497
## 240 0.3800530295 0.00011305296980 0.004541565054 0.519911149 0.01139833099
## 241 0.3789579601 0.00002188443796 0.003597421376 0.519261837 0.00478626746
## 242 0.4683877791 0.00009729882509 0.002228437191 0.464617354 0.00952434185
## 243 0.4683877791 0.00009729882509 0.002228437191 0.464617354 0.00952434185
## 244 0.3953504328 0.00055104791948 0.004944773765 0.505928771 0.02569532304
## 245 0.1035315817 0.00004304008724 0.045352185324 0.514599554 0.00830346333
## 246 0.5493887271 0.00079148845264 0.001420064682 0.394749921 0.02537446228
## 247 0.7467070371 0.01891036351433 0.000104385639 0.158039203 0.07378469067
## 248 0.7374608866 0.00419419676932 0.000190138295 0.213212873 0.03982369324
## 249 0.0316486189 0.00001711450278 0.119745873392 0.349277184 0.00477300383
## 250 0.0307865833 0.00000048935604 0.063986541186 0.318713375 0.00070190985
## 251 0.1144657896 0.00084104177707 0.063742831805 0.527532730 0.04017204292
## 252 0.2133965166 0.00004327072417 0.014981135612 0.569718853 0.00793518653
## 253 0.1099901086 0.00002162145450 0.037041347525 0.518514065 0.00572980368
## 254 0.3576531227 0.00010419098228 0.005380772801 0.532167484 0.01117740206
## 255 0.4603650956 0.00008509693865 0.002333747218 0.470120142 0.00896410521
## 256 0.7324357553 0.00612866251102 0.000191139955 0.208448533 0.04802675153
## 257 0.4917567614 0.00020126426199 0.002021621727 0.446879168 0.01353157809
## 258 0.5624493715 0.00102301241663 0.001282280467 0.381734636 0.02832140868
## 259 0.4276786938 0.00018437725528 0.003339292897 0.490977807 0.01398349616
## 260 0.5853590187 0.00159422856080 0.001057135590 0.357970760 0.03428199197
## 261 0.5853590187 0.00159422856080 0.001057135590 0.357970760 0.03428199197
## 262 0.7504654860 0.00159774776285 0.000169656783 0.217906794 0.02418069809
## 263 0.0370958436 0.00000118830535 0.065514896736 0.352104913 0.00114820094
## 264 0.6109785729 0.00002757141108 0.000563092758 0.356690165 0.00402000971
## 265 0.2039898748 0.00000986005928 0.012742490255 0.557812678 0.00362250250
## 266 0.2417565148 0.00007818968744 0.013072275445 0.569666718 0.01063557541
## 267 0.0370958436 0.00000118830535 0.065514896736 0.352104913 0.00114820094
## 268 0.0345674450 0.00005284342363 0.139455414578 0.356006799 0.00875747331
## 269 0.4597458885 0.00002269051360 0.001949351900 0.468758996 0.00447264509
## 270 0.6836563551 0.00065387718322 0.000373554412 0.285473619 0.01810942772
## 271 0.6553614521 0.00034990239543 0.000487667343 0.313982380 0.01395607943
## 272 0.1461209708 0.00339452090161 0.029680532444 0.550745508 0.08249015518
## 273 0.4403030295 0.00011732255662 0.002857663918 0.483294207 0.01086774766
## 274 0.5218657035 0.00009148130747 0.001405574098 0.426773341 0.00858791538
## 275 0.0138517969 0.00000114914825 0.136064325208 0.234028295 0.00101474497
## 276 0.6600294522 0.00745849010715 0.000445551518 0.261411683 0.06231806337
## 277 0.3420124650 0.00056440705164 0.007606160472 0.534787215 0.02754400179
## 278 0.7437144424 0.01328653940778 0.000128353667 0.175110489 0.06469071941
## 279 0.6600294522 0.00745849010715 0.000445551518 0.261411683 0.06231806337
## 280 0.3732633916 0.00019057096596 0.005174558457 0.522954391 0.01510396438
## 281 0.4633217799 0.00095517516640 0.002998326171 0.456618392 0.03147493467
## 282 0.7730702864 0.00614683951125 0.000106310192 0.174795530 0.04275012134
## 283 0.2758738666 0.00008289422849 0.009951094276 0.563075842 0.01067288490
## 284 0.2401693489 0.00004035491394 0.011895092122 0.567692586 0.00748924222
## 285 0.5687880231 0.00027708103446 0.001088547888 0.388082905 0.01317308960
## 286 0.5698602468 0.00020119084049 0.001039626660 0.387387448 0.01211925186
## 287 0.7540706685 0.00969995013506 0.000124552686 0.177725426 0.05517631540
## 288 0.1013485158 0.00001578581479 0.038751076550 0.506576829 0.00484283227
## 289 0.6303903548 0.00063491521798 0.000652654561 0.331147597 0.01980367948
## 290 0.7524998657 0.00861362765215 0.000132461930 0.182606005 0.05271925411
## 291 0.1385455699 0.00005467905954 0.031952996253 0.547282100 0.00935728492
## 292 0.4486202341 0.00073386649987 0.003298843614 0.469602410 0.02798244154
## 293 0.3184136514 0.00005178028496 0.006589095243 0.547517707 0.00801212812
## 294 0.6688477095 0.01137283138767 0.000361610004 0.239261586 0.07354395758
## 295 0.2335200314 0.00545351842277 0.022165677798 0.539300462 0.09742306633
## 296 0.4928364979 0.06341729257309 0.000839839119 0.242580711 0.19236731432
## 297 0.1173691621 0.00000328813596 0.024050505065 0.511409437 0.00208235863
## 298 0.2335200314 0.00545351842277 0.022165677798 0.539300462 0.09742306633
## 299 0.3106380806 0.00119570418118 0.010616250359 0.541847285 0.04195790953
## 300 0.5445992487 0.00894023443390 0.001414980147 0.343180534 0.08411496448
## 301 0.6703506540 0.00070491216001 0.000434459201 0.296309721 0.01934372884
## 302 0.5963318355 0.02059215650963 0.000659071382 0.263839283 0.10974079592
## 303 0.1173691621 0.00000328813596 0.024050505065 0.511409437 0.00208235863
## 304 0.7053696411 0.00107474860945 0.000298835710 0.261907542 0.02220534585
## 305 0.5376563899 0.00011904380848 0.001274356859 0.414371810 0.00964449556
## 306 0.4603155557 0.00011820305240 0.002439159764 0.469981929 0.01065330130
## 307 0.5376563899 0.00011904380848 0.001274356859 0.414371810 0.00964449556
## 308 0.5746827087 0.00285086959249 0.001178946115 0.355156041 0.04648118951
## 309 0.2034571201 0.00006303133574 0.017575485445 0.569818329 0.00974796305
## 310 0.7207610600 0.00242726298625 0.000247372155 0.237674060 0.03203296876
## 311 0.4035446962 0.00003306010801 0.002120803037 0.505981993 0.00580031365
## 312 0.3639569835 0.00173280903253 0.007043518031 0.512222048 0.04810975425
## 313 0.0802665367 0.00007644161430 0.068080239595 0.487750205 0.01126785359
## 314 0.2947701521 0.00004686531395 0.007750826650 0.556427136 0.00776712385
## 315 0.2216753208 0.00009753495304 0.015926142133 0.572941413 0.01215118131
## 316 0.4819685398 0.00046937347116 0.002409202315 0.449855018 0.02129688695
## 317 0.2401330158 0.00004027064502 0.011900187133 0.567033783 0.00748362870
## 318 0.2401330158 0.00004027064502 0.011900187133 0.567033783 0.00748362870
## 319 0.5493887271 0.00079148845264 0.001420064682 0.394749921 0.02537446228
## 320 0.1340751859 0.00001028728624 0.024790898954 0.533712708 0.00382563180
## 321 0.0977383002 0.00000234659113 0.027796728459 0.489136806 0.00174766760
## 322 0.0305615521 0.00004327294769 0.117103885347 0.350350310 0.00785672984
## 323 0.5018501961 0.00096211962893 0.002167777880 0.428917541 0.02999657055
## 324 0.6724198051 0.00238422576953 0.000437473086 0.279039304 0.03544209112
## 325 0.7535458753 0.00064102717524 0.000165338726 0.215436956 0.02469985284
## 326 0.1736526856 0.00004169470472 0.021532538315 0.562115625 0.00796358989
## 327 0.7186545143 0.00228332012756 0.000254613312 0.240470347 0.03125612958
## 328 0.5524445350 0.00005421612839 0.001031585891 0.403274760 0.00626735489
## 329 0.1811625411 0.00009424590520 0.023014451594 0.567904793 0.01224638339
## 330 0.0039775658 0.00000074800407 0.247106314095 0.123191883 0.00066781172
## 331 0.5865353550 0.00009747196605 0.000823321935 0.375844973 0.00809378596
## 332 0.6887042856 0.02146858739625 0.000214332892 0.195346029 0.09026797416
## 333 0.2292309786 0.00001767715205 0.011207775379 0.564635920 0.00486987956
## 334 0.2240520083 0.00018976662284 0.017466656677 0.573011754 0.01724960108
## 335 0.3106748812 0.00004495408860 0.006850559432 0.549972971 0.00748603158
## 336 0.5791169486 0.00323383165407 0.001119027139 0.349053740 0.04895336704
## 337 0.3905711298 0.00002034105104 0.003192159517 0.511619670 0.00454727911
## 338 0.1169706736 0.00000323869563 0.024109553281 0.508200007 0.00207028721
## 339 0.6032057204 0.00364008426635 0.000889240840 0.326904750 0.04988399109
## 340 0.5197170721 0.00003387321284 0.001233644150 0.428825581 0.00510187732
## 341 0.0651430634 0.00000316463946 0.045320803863 0.436269183 0.00202015530
## 342 0.5373266164 0.00008562034704 0.001220641967 0.415203958 0.00811557706
## 343 0.6148141363 0.00318804189067 0.000792163644 0.320846689 0.04568805117
## 344 0.6025112923 0.00233232545485 0.000906540994 0.337004574 0.04016857995
## 345 0.3725691707 0.00007106531541 0.004528650694 0.523197395 0.00898855924
## 346 0.0106727368 0.00000132517106 0.165151315749 0.208427193 0.00105858098
## 347 0.0084743614 0.00000091583124 0.177181610417 0.186087373 0.00084050212
## 348 0.2821533862 0.00131507661465 0.013579109042 0.551232779 0.04528880394
## 349 0.0586576222 0.00000740937619 0.061492585636 0.426160668 0.00318499586
## 350 0.1768668952 0.00001788304919 0.016638258187 0.558695433 0.00505377714
## 351 0.7272423932 0.00081531448677 0.000230440381 0.245262605 0.01842954893
## 352 0.1526899364 0.00001335777805 0.021273411309 0.548281405 0.00438605349
## 353 0.1768668952 0.00001788304919 0.016638258187 0.558695433 0.00505377714
## 354 0.2821533862 0.00131507661465 0.013579109042 0.551232779 0.04528880394
## 355 0.7272423932 0.00081531448677 0.000230440381 0.245262605 0.01842954893
## 356 0.0840694287 0.00001956688182 0.050356548429 0.484612177 0.00542744079
## 357 0.0840694287 0.00001956688182 0.050356548429 0.484612177 0.00542744079
## 358 0.1459784722 0.00000426917280 0.018716007838 0.534936306 0.00237800522
## 359 0.0557669280 0.00002090312827 0.078603436541 0.428395875 0.00553744703
## 360 0.1459784722 0.00000426917280 0.018716007838 0.534936306 0.00237800522
## 361 0.7084202358 0.00408551844059 0.000280271075 0.237920343 0.04251848358
## 362 0.0906682001 0.00000417951662 0.034526323824 0.482423272 0.00236953460
## 363 0.5248762830 0.00102692940147 0.001792009146 0.410783571 0.03005033739
## 364 0.5002953597 0.00011930092072 0.001766478611 0.441844078 0.01017858842
## 365 0.1394412424 0.00007643935214 0.033523756755 0.550791836 0.01118072766
## 366 0.1394412424 0.00007643935214 0.033523756755 0.550791836 0.01118072766
## 367 0.7341170460 0.00371609591813 0.000201700080 0.218537631 0.03796255280
## 368 0.1718109428 0.00026330229226 0.029597160376 0.567040610 0.02124708227
## 369 0.8419264379 0.00097943723264 0.000037346511 0.131117744 0.02425860960
## 370 0.3073196370 0.00001187927398 0.005689038337 0.544427098 0.00370465426
## 371 0.5682798972 0.00014446755737 0.000999803861 0.390022324 0.01019894454
## 372 0.5681677421 0.00020059922250 0.001043277609 0.389025812 0.01210946596
## 373 0.1308915969 0.00001714941926 0.028207437630 0.534545598 0.00504571824
## 374 0.7080653334 0.00262143535639 0.000288300919 0.247212222 0.03428293201
## 375 0.2208547716 0.00000562545418 0.010008553823 0.553988325 0.00265608884
## 376 0.3399357832 0.00005641613001 0.005592550576 0.539344257 0.00822077684
## 377 0.3399357832 0.00005641613001 0.005592550576 0.539344257 0.00822077684
## 378 0.0103204955 0.00000027106406 0.122841555943 0.196000903 0.00044925241
## 379 0.4911885944 0.00027905024592 0.002110379726 0.446088179 0.01605868690
## 380 0.2208547716 0.00000562545418 0.010008553823 0.553988325 0.00265608884
## 381 0.7080653334 0.00262143535639 0.000288300919 0.247212222 0.03428293201
## 382 0.5392026141 0.01466341693909 0.001328298551 0.323777517 0.10568949531
## 383 0.0555476209 0.00000453048656 0.058941708598 0.416831064 0.00243074803
## 384 0.2350380953 0.00013059493161 0.014864970502 0.572668521 0.01404375709
## 385 0.6518542187 0.00145104690530 0.000546706958 0.304061618 0.02890309426
## 386 0.2486870522 0.00009037223286 0.012619720408 0.569130101 0.01142358822
## 387 0.6533382813 0.00584678768459 0.000500671440 0.274271064 0.05655131199
## 388 0.3912720996 0.00145810177483 0.005579419733 0.497759550 0.04264245680
## 389 0.7194002525 0.00007870370962 0.000208753043 0.263142607 0.00564397148
## 390 0.6791716101 0.00041657345509 0.000378693461 0.292646689 0.01450989464
## 391 0.6498068825 0.00092764905235 0.000552993341 0.311037547 0.02322145112
## 392 0.2051088506 0.00001379185607 0.013378327023 0.560585736 0.00433047805
## 393 0.2953120142 0.00003382673756 0.007394176328 0.554066761 0.00652504909
## 394 0.6251075587 0.00056031346126 0.000686117696 0.336446171 0.01879243017
## 395 0.4522397247 0.00014352527302 0.002668641894 0.475186885 0.01191093243
## 396 0.4757889323 0.00021415869432 0.002320896818 0.458075864 0.01426708222
## 397 0.3680323949 0.10887840655759 0.001564883182 0.243514309 0.26782327157
## 398 0.3571953209 0.00109905898341 0.007116812379 0.521428163 0.03824382592
## 399 0.1652550226 0.00011684425696 0.027744816446 0.563791912 0.01385466344
## 400 0.3571953209 0.00109905898341 0.007116812379 0.521428163 0.03824382592
## 401 0.0377614926 0.00001066933741 0.098060616849 0.364789014 0.00375427261
## 402 0.2619933080 0.00044819996417 0.014062655768 0.567080013 0.02630427652
## 403 0.7001711340 0.00094960147229 0.000316111224 0.267716811 0.02111041176
## 404 0.3161195923 0.00001912941221 0.005737117215 0.544073376 0.00473474367
## 405 0.0377614926 0.00001066933741 0.098060616849 0.364789014 0.00375427261
## 406 0.2619933080 0.00044819996417 0.014062655768 0.567080013 0.02630427652
## 407 0.7901882666 0.00721008966743 0.000079098733 0.156448087 0.04362541172
## 408 0.3426930832 0.00021157818120 0.006683462486 0.538396425 0.01646362355
## 409 0.0853239983 0.00000828369303 0.042256392169 0.478803702 0.00342837949
## 410 0.1767048420 0.00001778316674 0.018045105129 0.557349518 0.00504340659
## 411 0.2120759275 0.00000667594163 0.010870942622 0.558243795 0.00292836983
## 412 0.5064456450 0.00036180888116 0.001918572662 0.434413019 0.01715027913
## 413 0.5755482668 0.00022832845112 0.000991558410 0.382614807 0.01280699003
## 414 0.5296384908 0.00027945290876 0.001521215423 0.417628473 0.01524004672
## 415 0.4032869632 0.00003288340908 0.003188021120 0.504535182 0.00579449306
## 416 0.5960620475 0.00046553803787 0.000885687678 0.362010481 0.01792453201
## 417 0.5960620475 0.00046553803787 0.000885687678 0.362010481 0.01792453201
## 418 0.3550779190 0.00002007455155 0.004266285079 0.530691916 0.00468146518
## 419 0.7533219982 0.00914107286668 0.000128453025 0.180158002 0.05393628835
## 420 0.0034648326 0.00000041543807 0.186819707827 0.121116395 0.00048511026
## 421 0.5375858954 0.00022956150117 0.001387829639 0.412413156 0.01359949346
## 422 0.1220149763 0.00001758158038 0.031196880254 0.527637729 0.00512583986
## 423 0.5375858954 0.00022956150117 0.001387829639 0.412413156 0.01359949346
## 424 0.7092597566 0.00056119060234 0.000275407104 0.264324497 0.01583201542
## 425 0.7097619515 0.00178671214486 0.000285548007 0.251807414 0.02831260566
## 426 0.5931992353 0.00015400275428 0.000817701205 0.369431975 0.01016662723
## 427 0.6091328363 0.00122497493925 0.000839806316 0.341730925 0.02877887178
## 428 0.7502718669 0.03107747572323 0.000065501249 0.132089867 0.08491729313
## 429 0.7384783641 0.00162931163558 0.000202140501 0.227955559 0.02532189261
## 430 0.3732633916 0.00019057096596 0.005174558457 0.522954391 0.01510396438
## 431 0.5195549770 0.00195709188790 0.001935329385 0.405182337 0.04197383328
## 432 0.0207563618 0.00000161238245 0.108982962329 0.277129626 0.00128027064
## 433 0.4862112973 0.00001980700617 0.001492578763 0.452238972 0.00401425743
## 434 0.5529852407 0.00011130752089 0.001105448435 0.402687233 0.00910447655
## 435 0.5997110542 0.00219032482403 0.000931556376 0.340434211 0.03912762733
## 436 0.6376569661 0.00156071125023 0.000634338893 0.314930307 0.03080422673
## 437 0.4032869632 0.00003288340908 0.003188021120 0.504535182 0.00579449306
## 438 0.5681448079 0.00010395760529 0.000957866988 0.390930136 0.00858541454
## 439 0.6625963750 0.00086060873116 0.000477964310 0.301078647 0.02174716036
## 440 0.6738845168 0.00110465515046 0.000428076426 0.288767754 0.02411160022
## 441 0.6657359329 0.00134787486062 0.000470733716 0.293303623 0.02709480277
## 442 0.5842380662 0.00366562133097 0.001059655292 0.342035977 0.05157523396
## 443 0.7450012105 0.00207618633861 0.000181648340 0.219167525 0.02790047377
## 444 0.7450012105 0.00207618633861 0.000181648340 0.219167525 0.02790047377
## 445 0.6832000012 0.00090632500898 0.000386890079 0.282718115 0.02146631144
## 446 0.7434688198 0.00533800948473 0.000168796366 0.202755574 0.04378397726
## 447 0.6832000012 0.00090632500898 0.000386890079 0.282718115 0.02146631144
## 448 0.4474759790 0.00002735360355 0.002145997326 0.478484580 0.00499376398
## 449 0.3190343357 0.00007206375319 0.006896866287 0.548211658 0.00953923471
## 450 0.4468919492 0.03862868556172 0.002255016916 0.330719457 0.16388845232
## 451 0.5057639547 0.00007018003808 0.001548135725 0.438917813 0.00763521567
## 452 0.7006882469 0.02820835605221 0.000151801929 0.171943926 0.09606027823
## 453 0.7231442863 0.04968858680553 0.000059922599 0.121761506 0.10401660695
## 454 0.7206172022 0.05251163308811 0.000057833531 0.119578887 0.10595588857
## 455 0.2716577585 0.00001575015948 0.007880541889 0.556257040 0.00443232368
## 456 0.6625963750 0.00086060873116 0.000477964310 0.301078647 0.02174716036
## 457 0.6625963750 0.00086060873116 0.000477964310 0.301078647 0.02174716036
## 458 0.6625963750 0.00086060873116 0.000477964310 0.301078647 0.02174716036
## 459 0.4868995572 0.01565314071532 0.002149839034 0.361554863 0.11282851057
## 460 0.2229354272 0.00009789579518 0.015916073203 0.572351828 0.01215611188
## 461 0.1678163160 0.00003585486937 0.022174930410 0.559323168 0.00737022256
## 462 0.5454051890 0.00018847400959 0.001265657480 0.407122927 0.01213013714
## 463 0.0002929587 0.00000001385058 0.367952731103 0.031750903 0.00005077962
## 464 0.0500890163 0.00000320060152 0.060896795370 0.397855779 0.00200883917
## 465 0.5970304920 0.00033595318248 0.000849637157 0.363299433 0.01511871248
## 466 0.6683187080 0.00097517703127 0.000452400255 0.294901946 0.02290227427
## 467 0.5454051890 0.00018847400959 0.001265657480 0.407122927 0.01213013714
## 468 0.3889613833 0.00025007194275 0.004743509670 0.514261705 0.01715838739
## 469 0.6103714974 0.00060159575416 0.000795340531 0.348134482 0.01999642473
## 470 0.6103714974 0.00060159575416 0.000795340531 0.348134482 0.01999642473
## 471 0.6675461330 0.00023355510483 0.000404900607 0.305794906 0.01101585681
## 472 0.6414043942 0.00113092874824 0.000608037341 0.315903133 0.02607980358
## 473 0.6319941404 0.00137692969231 0.000669908990 0.321397624 0.02923991042
## 474 0.6119746100 0.00043462168165 0.000762747755 0.349221808 0.01687356129
## 475 0.5715913567 0.00007517376671 0.000910852054 0.388123683 0.00723040097
## 476 0.5715913567 0.00007517376671 0.000910852054 0.388123683 0.00723040097
## 477 0.7244001799 0.00841570838404 0.000197532151 0.205949324 0.05645387853
## 478 0.7244001799 0.00841570838404 0.000197532151 0.205949324 0.05645387853
## 479 0.5931992353 0.00015400275428 0.000817701205 0.369431975 0.01016662723
## 480 0.6730478463 0.01625116881890 0.000300791430 0.220653159 0.08435435813
## 481 0.6539404560 0.01223431304068 0.000419568616 0.247791359 0.07838563432
## 482 0.4061738785 0.00001921741452 0.002800679767 0.502804007 0.00433926799
## 483 0.4203435898 0.00016136267351 0.003491318150 0.495668414 0.01314678999
## 484 0.5220612033 0.00024516765009 0.001598055129 0.423965655 0.01438861324
## 485 0.3951071473 0.00565000269784 0.005379096868 0.464218433 0.08309049913
## 486 0.6354304693 0.14195589700736 0.000027723510 0.079891979 0.14211814970
## 487 0.3951071473 0.00565000269784 0.005379096868 0.464218433 0.08309049913
## 488 0.7198502926 0.00377938127196 0.000242232905 0.230237610 0.03968659675
## 489 0.5529852407 0.00011130752089 0.001105448435 0.402687233 0.00910447655
## 490 0.6582022118 0.06637983148856 0.000108346381 0.139571806 0.13384554606
## 491 0.7092597566 0.00056119060234 0.000275407104 0.264324497 0.01583201542
## 492 0.6252064081 0.00040385770329 0.000660576905 0.338857709 0.01584137057
## 493 0.6185019896 0.00035560071054 0.000696450358 0.345240352 0.01501022088
## 494 0.0325678784 0.00000900215850 0.107204284228 0.343138485 0.00338104116
## 495 0.7005451673 0.00320101340921 0.000314997830 0.249660770 0.03856385163
## 496 0.6458231099 0.00188217620958 0.000584117996 0.305279653 0.03328810336
## 497 0.2331817078 0.00003479538695 0.012314133420 0.566891859 0.00695916035
## 498 0.5372827588 0.16460524296701 0.000090914006 0.105588415 0.19116289377
## 499 0.7005451673 0.00320101340921 0.000314997830 0.249660770 0.03856385163
## 500 0.7136535140 0.00296620386325 0.000270253406 0.240031305 0.03605273784
## 501 0.5076649082 0.00009669439063 0.001615963866 0.438014854 0.00786456884
## 502 0.7014394154 0.01170969668985 0.000244376132 0.213653577 0.06796592964
## 503 0.5247985775 0.20193270831468 0.000052388512 0.086837994 0.18557478480
## 504 0.6539404560 0.01223431304068 0.000419568616 0.247791359 0.07838563432
## 505 0.3805861870 0.00021830609775 0.004957113502 0.518824321 0.01610299325
## 506 0.1907139982 0.00016407004468 0.023022595186 0.571163242 0.01634472482
## 507 0.5259426712 0.17224213603936 0.000089777594 0.106666021 0.19381322871
## 508 0.6574268084 0.00111786412798 0.000511336400 0.302650288 0.02507868099
## 509 0.6787189373 0.00830613869713 0.000351077243 0.242961701 0.06275862954
## 510 0.4751330398 0.00021377968436 0.002322702441 0.458717353 0.01426199121
## 511 0.6346088950 0.00099639442390 0.000643960444 0.323194831 0.02472000569
## 512 0.7031756429 0.00361274780443 0.000299139844 0.245199359 0.04044727787
## 513 0.6346088950 0.00099639442390 0.000643960444 0.323194831 0.02472000569
## 514 0.6658330612 0.00044904910057 0.000440355749 0.303675398 0.01549628570
## 515 0.4727692261 0.00004107979077 0.001869962144 0.462186756 0.00600578820
## 516 0.5249496624 0.00222619823379 0.001839672850 0.398891356 0.04433320802
## 517 0.4079115544 0.00299416647741 0.005021439346 0.473628429 0.06016076287
## 518 0.6602375746 0.00118989985186 0.000497465980 0.299524987 0.02573545430
## 519 0.6602375746 0.00118989985186 0.000497465980 0.299524987 0.02573545430
## 520 0.1779892080 0.00515824457627 0.037762992259 0.533871839 0.09898592801
## 521 0.6285432849 0.00087846545773 0.000679641081 0.329625981 0.02344968472
## 522 0.3914691428 0.01641173023864 0.004700752967 0.416606217 0.13650986282
## 523 0.7145591028 0.02772208901559 0.000126337713 0.163147944 0.09184932666
## 524 0.5828216252 0.00025979257426 0.000942097680 0.376186296 0.01353997968
## 525 0.6925036568 0.00115998560690 0.000348022463 0.272207188 0.02374830272
## 526 0.6468578930 0.11838581051281 0.000039357158 0.092694765 0.14123949380
## 527 0.7088903710 0.00168267099751 0.000291869912 0.253220382 0.02764689678
## 528 0.7448227559 0.00566863205366 0.000163811063 0.200181293 0.04482531491
## 529 0.3457797260 0.00372455919303 0.008436992946 0.503470935 0.07203701171
## 530 0.6366582825 0.00072037555175 0.000618540390 0.324765176 0.02088209747
## 531 0.5376563899 0.00011904380848 0.001274356859 0.414371810 0.00964449556
## 532 0.6251075587 0.00056031346126 0.000686117696 0.336446171 0.01879243017
## 533 0.6167582019 0.00008460311071 0.000613142530 0.351362023 0.00715680100
## 534 0.5970341664 0.00046627132631 0.000883411297 0.361056182 0.01793976697
## 535 0.6251075587 0.00056031346126 0.000686117696 0.336446171 0.01879243017
## 536 0.6991901693 0.00319737945257 0.000316948186 0.251032950 0.03851372491
## 537 0.6625963750 0.00086060873116 0.000477964310 0.301078647 0.02174716036
## 538 0.6991901693 0.00319737945257 0.000316948186 0.251032950 0.03851372491
## 539 0.6873099370 0.00047327597051 0.000355755342 0.284523141 0.01529032415
## 540 0.5437241856 0.04005383268792 0.000775620217 0.252582164 0.15439967967
## 541 0.4471930312 0.06779556799161 0.001318990134 0.258989140 0.21418202321
## 542 0.6808429422 0.03814097947988 0.000154149008 0.166979810 0.11109942129
## 543 0.6808429422 0.03814097947988 0.000154149008 0.166979810 0.11109942129
## 544 0.6265962128 0.00020998840146 0.000607373530 0.341090834 0.01125806811
## 545 0.6261635212 0.00121437228158 0.000707233200 0.327887931 0.02774575000
## 546 0.6837033055 0.00324324982802 0.000379536556 0.263747996 0.04015433037
## 547 0.6299740826 0.00462501835912 0.000667170167 0.299339761 0.05326922508
## 548 0.7120251377 0.00461335951554 0.000264219941 0.232128606 0.04462288708
## 549 0.7084202358 0.00408551844059 0.000280271075 0.237920343 0.04251848358
## 550 0.6895976804 0.00160273120774 0.000362025201 0.270645332 0.02808672942
## 551 0.6327696966 0.00137795879939 0.000668086274 0.320615496 0.02927274190
## 552 0.7063997770 0.00714678184252 0.000259533151 0.225449010 0.05499632263
## 553 0.6327696966 0.00137795879939 0.000668086274 0.320615496 0.02927274190
## 554 0.7281645381 0.00511561296624 0.000209036050 0.216504995 0.04473880587
## 555 0.6792893806 0.00125094066857 0.000404939856 0.282678685 0.02537729841
## 556 0.5474814031 0.00013622403279 0.001208236867 0.406110509 0.01022394290
## 557 0.5659826716 0.14072471339136 0.000089534707 0.113150147 0.17871996402
## 558 0.5322042461 0.00172005000172 0.001725245660 0.397831301 0.03861623339
## 559 0.3336470112 0.00009509492326 0.006371078022 0.542978617 0.01090274377
## 560 0.5659736342 0.00053448202778 0.001182668112 0.385251465 0.02020137586
## 561 0.6805381500 0.00125268311747 0.000402918508 0.281437186 0.02540607667
## 562 0.5220612033 0.00024516765009 0.001598055129 0.423965655 0.01438861324
## 563 0.6115238413 0.00130407395058 0.000819222405 0.339058594 0.02952321197
## 564 0.5057639547 0.00007018003808 0.001548135725 0.438917813 0.00763521567
## 565 0.5267848349 0.00074286974387 0.001721540552 0.412851855 0.02539018142
## 566 0.5267848349 0.00074286974387 0.001721540552 0.412851855 0.02539018142
## 567 0.7248292152 0.00274235098548 0.000233448833 0.232127920 0.03363784652
## 568 0.4764583762 0.00008004946413 0.002035028869 0.459104921 0.00851131252
## 569 0.6770867365 0.00269716824878 0.000413413917 0.272888125 0.03726590725
## 570 0.6440008532 0.00176967860399 0.000598572100 0.307623633 0.03248018130
## 571 0.3936601183 0.00076314143144 0.005153582507 0.504476312 0.03046213249
## 572 0.0169921340 0.00000047836137 0.099129249865 0.248462893 0.00065033572
## 573 0.6203778404 0.04038172193151 0.000316827836 0.203176732 0.13118221263
## 574 0.5677743708 0.00027831826712 0.001088275265 0.387917656 0.01437019289
## 575 0.2263531924 0.00003014023517 0.012726385939 0.567227073 0.00647422470
## 576 0.3426930832 0.00021157818120 0.006683462486 0.538396425 0.01646362355
## 577 0.5421354596 0.00069550130031 0.001495228534 0.401735661 0.02398375852
## 578 0.0512722108 0.00005356434606 0.101335837225 0.422190562 0.00913336995
## 579 0.6354822080 0.00099745619270 0.000642026515 0.322323840 0.02474660721
## 580 0.5439862354 0.00050282776899 0.001433915529 0.402917775 0.02025658644
## 581 0.0154378333 0.00000035043834 0.100415256434 0.240154743 0.00054243753
## 582 0.6948170878 0.00083878769499 0.000334294489 0.273582288 0.02006402976
## 583 0.4201866751 0.00022389612706 0.003646592398 0.495065155 0.01561284654
## 584 0.3805861870 0.00021830609775 0.004957113502 0.518824321 0.01610299325
## 585 0.2983098250 0.00017646792196 0.009292072369 0.557093391 0.01560738279
## 586 0.1353358844 0.00001958211403 0.027528631068 0.540348892 0.00462663891
## 587 0.1620805526 0.00003081362814 0.022823493853 0.556212809 0.00681698528
## 588 0.6858026826 0.00344825249222 0.000368807676 0.260725230 0.04115921121
## 589 0.6780714794 0.00446477437018 0.000393319646 0.261295704 0.04732086583
## 590 0.0032949721 0.00000039529480 0.253182385790 0.115809841 0.00046307783
## 591 0.5796853639 0.00069089312510 0.001066747426 0.372093299 0.02255433430
## 592 0.4197818237 0.00022363431662 0.003647048859 0.495458546 0.01561426821
## 593 0.1321269414 0.00000525591571 0.022378732805 0.527297262 0.00266998188
## 594 0.3116882300 0.00012078244756 0.007856022343 0.552371816 0.01262988882
## 595 0.4298586638 0.00218841222889 0.004138681631 0.467395380 0.04924112107
## 596 0.3341993925 0.00068142406290 0.008274271612 0.537129402 0.03062475110
## 597 0.5071422959 0.00026058937672 0.001839383412 0.435626915 0.01419268760
## 598 0.0591742014 0.00000227452702 0.048506593066 0.420429479 0.00168295752
## 599 0.0591742014 0.00000227452702 0.048506593066 0.420429479 0.00168295752
## 600 0.1240795642 0.00000754062080 0.026047610870 0.523689999 0.00324700311
## 601 0.5467913834 0.00236818941890 0.001515872869 0.380970908 0.04423859918
## 602 0.2983098250 0.00017646792196 0.009292072369 0.557093391 0.01560738279
## 603 0.7281645381 0.00511561296624 0.000209036050 0.216504995 0.04473880587
## 604 0.6392701175 0.00243946800199 0.000623589073 0.306265891 0.03830881950
## 605 0.6402447452 0.00380496514199 0.000609133798 0.295932548 0.04758182731
## 606 0.6392701175 0.00243946800199 0.000623589073 0.306265891 0.03830881950
## 607 0.5681677421 0.00020059922250 0.001043277609 0.389025812 0.01210946596
## 608 0.5681677421 0.00020059922250 0.001043277609 0.389025812 0.01210946596
## 609 0.2006936883 0.00002320016764 0.015213463184 0.563896969 0.00573196713
## 610 0.2006936883 0.00002320016764 0.015213463184 0.563896969 0.00573196713
## 611 0.2006936883 0.00002320016764 0.015213463184 0.563896969 0.00573196713
## 612 0.0898238898 0.00000298022159 0.032744140776 0.478579923 0.00197532692
## 613 0.2042819733 0.00000994944999 0.012714254490 0.560079225 0.00363438653
## 614 0.6859900900 0.00221226418410 0.000376230992 0.268854203 0.03318701720
## 615 0.3487387753 0.00009036346287 0.005615219134 0.535958052 0.01045725391
## 616 0.6484544577 0.00200308200507 0.000568179141 0.302077720 0.03415406565
## 617 0.6560646024 0.00241319082855 0.000522650646 0.292521690 0.03687080947
## 618 0.4106514033 0.00052109628996 0.004333648282 0.497138899 0.02452756930
## 619 0.3870746522 0.00004755993305 0.003810512911 0.515842604 0.00618563767
## 620 0.0598324634 0.00000319497658 0.051229304502 0.424521704 0.00202210518
## 621 0.2578738889 0.00154356306527 0.016940093998 0.555920844 0.05028577405
## 622 0.3397674342 0.00220986099616 0.008726111746 0.518735488 0.05577230908
## 623 0.0452729793 0.00000228707408 0.062573724419 0.381299731 0.00166469071
## 624 0.5819888480 0.00149955774048 0.001094437494 0.360967845 0.03335712160
## 625 0.5505162577 0.00653051877952 0.001380446707 0.351201707 0.07175181082
## 626 0.5894881570 0.01275166490066 0.000842345663 0.294151098 0.09117099117
## 627 0.2844391338 0.00035647484807 0.011472690816 0.560559428 0.02288589500
## 628 0.7212779674 0.06409546350701 0.000041543851 0.105039459 0.10858641020
## 629 0.6677474590 0.00328600591444 0.000453921164 0.276779381 0.04181597197
## 630 0.6280054947 0.00029211275791 0.000630271373 0.338140338 0.01336791665
## 631 0.6677474590 0.00328600591444 0.000453921164 0.276779381 0.04181597197
## 632 0.6349248546 0.00215484886977 0.000657130304 0.311930997 0.03643225158
## 633 0.4100431826 0.00072119104422 0.004516980708 0.494780011 0.02905383562
## 634 0.0217785522 0.00000099859650 0.096518057207 0.283832890 0.00099258211
## 635 0.4100431826 0.00072119104422 0.004516980708 0.494780011 0.02905383562
## 636 0.0217785522 0.00000099859650 0.096518057207 0.283832890 0.00099258211
## 637 0.0580847143 0.00000306599518 0.051468818026 0.423043577 0.00198745284
## 638 0.4195158361 0.00121421764008 0.004373993282 0.483930428 0.03765871931
## 639 0.4195158361 0.00121421764008 0.004373993282 0.483930428 0.03765871931
## 640 0.3873866735 0.00066449564258 0.005383287915 0.510021310 0.02863214109
## 641 0.3049112808 0.00028172875893 0.009341522996 0.554528195 0.01985250742
## 642 0.2180439594 0.00022820681684 0.018928872415 0.573234504 0.01910276509
## 643 0.3352066485 0.00025566719085 0.007277219102 0.541537443 0.01832135101
## 644 0.0101987069 0.00000089500810 0.154886429511 0.198468609 0.00085159792
## 645 0.1522643644 0.00000689589181 0.019219359189 0.539977076 0.00307336825
## 646 0.0713586864 0.00002000632760 0.060591711834 0.460400720 0.00547694733
## 647 0.0533383844 0.00000292219215 0.056402689107 0.405953431 0.00192044302
## 648 0.4246784356 0.00301884501475 0.004298398606 0.463456075 0.05900286481
## 649 0.4123498941 0.00010146325913 0.003492349416 0.500916127 0.01039454552
## 650 0.1828017491 0.00002067907973 0.017516153664 0.560027052 0.00544843740
## 651 0.2003393915 0.00039591384078 0.024035993610 0.571853944 0.02587837137
## 652 0.3180212418 0.00007177533761 0.006900816500 0.549143814 0.00953488342
## 653 0.2003393915 0.00039591384078 0.024035993610 0.571853944 0.02587837137
## 654 0.2150891475 0.00008421220029 0.016471173632 0.572589718 0.01129285133
## 655 0.1081528183 0.00000239769469 0.025164789082 0.499695745 0.00175920289
## 656 0.2556756887 0.00000841940691 0.008065781709 0.554836131 0.00321465672
## 657 0.7556259489 0.00464609504628 0.000148953656 0.197151150 0.03821065753
## 658 0.2150891475 0.00008421220029 0.016471173632 0.572589718 0.01129285133
## 659 0.0503643002 0.00000325274403 0.060692808509 0.400777832 0.00202131193
## 660 0.6231587481 0.00167711597981 0.000735306528 0.325885352 0.03279999509
## 661 0.5474814031 0.00013622403279 0.001208236867 0.406110509 0.01022394290
## 662 0.4698763807 0.00108995356194 0.002856877077 0.450556742 0.03336415894
## 663 0.0567491556 0.00000266547293 0.052203178631 0.413846409 0.00183434364
## 664 0.1730111882 0.00005776321521 0.022627032867 0.565341640 0.00949004361
## 665 0.0173393629 0.00000049765931 0.099229656621 0.254265459 0.00066447552
## 666 0.4525006172 0.00014360790875 0.002659316451 0.476082833 0.01192265669
## 667 0.0375649330 0.00000167220917 0.069294122652 0.356061951 0.00138181540
## 668 0.0840694287 0.00001956688182 0.050356548429 0.484612177 0.00542744079
## 669 0.0214869778 0.00000070882015 0.091151705148 0.280400876 0.00082390873
## 670 0.0188978132 0.00000010619944 0.068919915390 0.256061080 0.00029191575
## 671 0.0188978132 0.00000010619944 0.068919915390 0.256061080 0.00029191575
## 672 0.0214869778 0.00000070882015 0.091151705148 0.280400876 0.00082390873
## 673 0.1240795642 0.00000754062080 0.026047610870 0.523689999 0.00324700311
## 674 0.0461120760 0.00000327520002 0.065931174079 0.388619951 0.00201677975
## 675 0.1916351326 0.00000423663935 0.011935826886 0.552380321 0.00232457993
## 676 0.1350024238 0.00001430734830 0.026137216236 0.536071037 0.00457303979
## 677 0.2639112754 0.00032282169332 0.013441608044 0.567629745 0.02214424240
## 678 0.2356182198 0.00009416044724 0.014182197804 0.570934016 0.01179372172
## 679 0.6925036568 0.00115998560690 0.000348022463 0.272207188 0.02374830272
## 680 0.6602375746 0.00118989985186 0.000497465980 0.299524987 0.02573545430
## 681 0.2356182198 0.00009416044724 0.014182197804 0.570934016 0.01179372172
## 682 0.7510692605 0.00411872203933 0.000158095474 0.202107686 0.03804392166
## 683 0.0537893550 0.00000747204320 0.066966726023 0.413819732 0.00318359482
## 684 0.1412613938 0.00000505811749 0.020260628599 0.530967932 0.00261373334
## 685 0.0438401075 0.00000277073023 0.066802451875 0.380481329 0.00183623653
## 686 0.5757810003 0.00016448950695 0.000950368330 0.383650620 0.01078881155
## 687 0.7409050967 0.00183785248524 0.000192513231 0.224439763 0.02657313388
## 688 0.1564341275 0.00002653809332 0.023446925097 0.553263995 0.00630282124
## 689 0.3887273423 0.00025047739352 0.004746600244 0.513696891 0.01714875171
## 690 0.6557948459 0.03130497369584 0.000253214880 0.196958846 0.11155001486
## 691 0.0364616733 0.00001771919933 0.110645880679 0.361636212 0.00491120932
## 692 0.3887273423 0.00025047739352 0.004746600244 0.513696891 0.01714875171
## 693 0.2705785332 0.00037296186259 0.012947134516 0.564465428 0.02372502374
## 694 0.5373266164 0.00008562034704 0.001220641967 0.415203958 0.00811557706
## 695 0.5606800248 0.00012683335797 0.001051471544 0.396369978 0.00963791738
## 696 0.1608200562 0.00000868503765 0.018445939813 0.545965547 0.00346469202
## 697 0.5606800248 0.00012683335797 0.001051471544 0.396369978 0.00963791738
## 698 0.5213359524 0.00006575073159 0.001346014130 0.427516968 0.00722314726
## 699 0.5213359524 0.00006575073159 0.001346014130 0.427516968 0.00722314726
## 700 0.5538827257 0.00124358699855 0.001403572701 0.385920358 0.03172852994
## 701 0.7723299600 0.03539906405377 0.000039584975 0.109512357 0.08165762903
## 702 0.0808429928 0.00028792291947 0.083421944229 0.485402612 0.02281208636
## 703 0.1420709134 0.00003273770515 0.028113796386 0.547129332 0.00709542558
## 704 0.3800530295 0.00011305296980 0.004541565054 0.519911149 0.01139833099
## 705 0.0180505466 0.00000139137904 0.117875885072 0.263732298 0.00116185217
## 706 0.0620726858 0.00000268165420 0.047745154323 0.428266387 0.00184352643
## 707 0.0650789765 0.00000315983547 0.046971981249 0.436024877 0.00201832450
## 708 0.0555476209 0.00000453048656 0.058941708598 0.416831064 0.00243074803
## 709 0.0250664306 0.00000084526897 0.084046092324 0.299843451 0.00092094562
## 710 0.2726289372 0.00002198609306 0.008260665861 0.558079657 0.00528872607
## 711 0.5598626201 0.14770236289872 0.000085778201 0.110309101 0.18076736602
## 712 0.6342328363 0.00523134074230 0.000630067814 0.292530079 0.05599967015
## 713 0.2914372306 0.00041032731416 0.011032408273 0.557722763 0.02448417653
## 714 0.0275246823 0.00000279913086 0.098778465746 0.318515836 0.00177287715
## 715 0.0069767895 0.00000006450616 0.117533965722 0.158422797 0.00019640325
## 716 0.4118129046 0.00027110257072 0.003981720406 0.499785260 0.01742720188
## 717 0.0662558739 0.00000615475897 0.052526456291 0.442034488 0.00289977868
## 718 0.0662558739 0.00000615475897 0.052526456291 0.442034488 0.00289977868
## 719 0.3960809365 0.00028679247224 0.004542523411 0.509088671 0.01826675559
## 720 0.0308320842 0.00000163764283 0.081153775664 0.327108534 0.00134621047
## 721 0.4492763690 0.00073143578647 0.003291523438 0.469856747 0.02798892282
## 722 0.5952496354 0.01828504382204 0.000701560886 0.271415678 0.10487930423
## 723 0.0182674352 0.00000080571133 0.104364514038 0.259027450 0.00086853235
## 724 0.3395492387 0.00545173593594 0.008539873017 0.497050971 0.08701823871
## 725 0.4492763690 0.00073143578647 0.003291523438 0.469856747 0.02798892282
## 726 0.7165056796 0.02331851925608 0.000139382214 0.170895861 0.08624906641
## 727 0.6983212733 0.00923970479447 0.000273608056 0.223368420 0.06313365694
## 728 0.2671366929 0.00003712289231 0.009399171891 0.563099958 0.00701620955
## 729 0.0217785522 0.00000099859650 0.096518057207 0.283832890 0.00099258211
## 730 0.0323411493 0.00000466847660 0.095885599968 0.342680766 0.00237181854
## 731 0.2562345629 0.00014522942562 0.012748870948 0.568733519 0.01460029177
## 732 0.5451910176 0.00036314419272 0.001375037716 0.404115052 0.01708833893
## 733 0.0512123364 0.00000632742757 0.067965593888 0.405718170 0.00290251112
## 734 0.1629828338 0.00004303566714 0.023975547506 0.558600618 0.00814497003
## 735 0.5244341327 0.00842959119374 0.001723127448 0.360374535 0.08453940859
## 736 0.3566750050 0.00010385458242 0.005387615252 0.532164836 0.01116568983
## 737 0.2909967726 0.00015322433302 0.009658134344 0.559565956 0.01458125170
## 738 0.3202522567 0.00019354213988 0.007893341659 0.548565646 0.01605579136
## 739 0.6532659327 0.00034899850238 0.000490727610 0.316020137 0.01394727196
## 740 0.0532728435 0.00007102273846 0.098223778243 0.430544684 0.01064267768
## 741 0.0178170475 0.00000098816853 0.111312706870 0.260610531 0.00096475717
## 742 0.7614650735 0.00337826301247 0.000142001017 0.196838288 0.03384370958
## 743 0.0885224103 0.00003213211753 0.051998500899 0.494951008 0.00708982368
## 744 0.6474032144 0.00022168101940 0.000496435932 0.323287283 0.01114754047
## 745 0.1819415824 0.00013125646979 0.024119280027 0.570169064 0.01460086211
## 746 0.0885224103 0.00003213211753 0.051998500899 0.494951008 0.00708982368
## 747 0.3143272686 0.00000980990014 0.005225659043 0.540813521 0.00332641430
## 748 0.0355027513 0.00000465916269 0.086226569528 0.354673272 0.00238669925
## 749 0.1308915969 0.00001714941926 0.028207437630 0.534545598 0.00504571824
## 750 0.0540228512 0.00000225598227 0.052987725418 0.405662105 0.00167198085
## 751 0.2728304603 0.00218685300657 0.015294021001 0.546116887 0.05930649840
## 752 0.5382502086 0.00132879784983 0.001618157191 0.397158626 0.03359526374
## 753 0.0642032698 0.00003413824551 0.074798861896 0.451695511 0.00725865058
## 754 0.1697682036 0.00001100286250 0.017649349057 0.552752745 0.00390926460
## 755 0.4078652683 0.00099812626170 0.004704362743 0.493023411 0.03441284373
## 756 0.1489770308 0.00001167837111 0.021795044980 0.543900018 0.00407346956
## 757 0.7695172923 0.01522731350760 0.000080399705 0.150188034 0.06285574469
## 758 0.4315459286 0.00107590932568 0.003929094671 0.476712327 0.03480697702
## 759 0.1028673019 0.00000667608049 0.032506743510 0.500495108 0.00305744574
## 760 0.1874269430 0.00002380772647 0.017009394222 0.563689368 0.00586704400
## 761 0.0279773659 0.00000119223111 0.082562994594 0.315523628 0.00112093400
## 762 0.0279773659 0.00000119223111 0.082562994594 0.315523628 0.00112093400
## 763 0.5321476102 0.00010483960096 0.001333274129 0.418347373 0.00911186822
## 764 0.0767629247 0.00001408261550 0.052504565956 0.468439418 0.00454740356
## 765 0.5821527959 0.00036022138116 0.000982415358 0.374987714 0.01606035326
## 766 0.0884443696 0.00000699721576 0.039163408874 0.483292081 0.00312498301
## 767 0.0737631573 0.00000108350946 0.033526270736 0.440957681 0.00114321378
## 768 0.4605657279 0.00011835765651 0.002428469925 0.471026043 0.01066470231
## 769 0.4605657279 0.00011835765651 0.002428469925 0.471026043 0.01066470231
## 770 0.0468612342 0.00000139587297 0.051894993349 0.384788143 0.00127505258
## 771 0.1117394554 0.00000916737020 0.031076636745 0.512841276 0.00362207723
## 772 0.2054675909 0.00017035402177 0.020250023178 0.572830429 0.01651034324
## 773 0.0455724147 0.00000233025967 0.062374754548 0.384579120 0.00167737289
## 774 0.4765224966 0.00394875509346 0.002812071418 0.420652876 0.06316393096
## 775 0.4765224966 0.00394875509346 0.002812071418 0.420652876 0.06316393096
## 776 0.6506990803 0.00066963989262 0.000533033584 0.313548044 0.01959121836
## 777 0.0295684970 0.00000194268281 0.086709102357 0.322632081 0.00146986868
## 778 0.1035315817 0.00004304008724 0.045352185324 0.514599554 0.00830346333
## 779 0.1035315817 0.00004304008724 0.045352185324 0.514599554 0.00830346333
## 780 0.0603059440 0.00002045276028 0.072267340027 0.436214105 0.00550099758
## 781 0.0518863980 0.00004680285630 0.093968328492 0.424786682 0.00850718656
## 782 0.0650789765 0.00000315983547 0.046971981249 0.436024877 0.00201832450
## 783 0.7309299213 0.01522485679784 0.000145017331 0.179629844 0.07081375813
## 784 0.2979262220 0.00012697211593 0.008883025189 0.557095678 0.01312129461
## 785 0.5283057209 0.00053696220900 0.001652384399 0.414516902 0.02143608597
## 786 0.0520675773 0.00000900577834 0.071574763691 0.412792634 0.00350569754
## 787 0.1648235320 0.00001312586412 0.019119113539 0.551053391 0.00431361306
## 788 0.0056898310 0.00000057693984 0.198172986559 0.148120369 0.00061684774
## 789 0.3452027715 0.00171297548099 0.008218790773 0.520640982 0.04870977016
## 790 0.3452027715 0.00171297548099 0.008218790773 0.520640982 0.04870977016
## 791 0.3452027715 0.00171297548099 0.008218790773 0.520640982 0.04870977016
## 792 0.0091168951 0.00000087987733 0.164897573638 0.187980036 0.00083038172
## 793 0.0683180734 0.00017386096786 0.088910504352 0.469991868 0.01735365741
## 794 0.0309513411 0.00001830340509 0.111288596312 0.345436838 0.00493484959
## 795 0.0656566260 0.00002024537563 0.066222652356 0.448528496 0.00549350421
## 796 0.0822358398 0.00000215945528 0.034043686651 0.464619621 0.00165819110
## 797 0.3457797260 0.00372455919303 0.008436992946 0.503470935 0.07203701171
## 798 0.4513179649 0.00014315015609 0.002671339797 0.476079192 0.01190528942
## 799 0.4862721620 0.00102406891990 0.002489583715 0.439486528 0.03165525133
## 800 0.0046524730 0.00000030098577 0.202115275010 0.137115847 0.00042303663
## 801 0.2229354272 0.00009789579518 0.015916073203 0.572351828 0.01215611188
## 802 0.0517088965 0.00000194487578 0.053589314948 0.400660637 0.00153470397
## 803 0.0382213227 0.00000097438087 0.061369520671 0.351897060 0.00103739339
## 804 0.2112190216 0.00014172163026 0.018694185179 0.572815776 0.01491536890
## 805 0.6406722925 0.00019521715044 0.000523630520 0.329522138 0.01056579409
## 806 0.6251075587 0.00056031346126 0.000686117696 0.336446171 0.01879243017
## 807 0.5474814031 0.00013622403279 0.001208236867 0.406110509 0.01022394290
## 808 0.1952930165 0.00002790946486 0.016476678386 0.564426407 0.00634732868
## 809 0.1686975009 0.00005005679902 0.023287892866 0.561529538 0.00880303693
## 810 0.2838380723 0.00000770885371 0.006360424336 0.548438367 0.00300149140
## 811 0.3376656786 0.00002900153307 0.005108747820 0.538574856 0.00578847438
## 812 0.1885175682 0.00000371258091 0.012275186156 0.546601773 0.00216836915
## 813 0.5524184958 0.00041336706097 0.001307533842 0.397675606 0.01807952627
## 814 0.0939849808 0.00001609852767 0.042626119993 0.497459285 0.00489364760
## 815 0.1090641003 0.00001537604725 0.035264916629 0.514206066 0.00478168521
## 816 0.0552943854 0.00000446800664 0.059143319802 0.414213022 0.00241861066
## 817 0.0831134102 0.00008881846980 0.066805544973 0.488484713 0.01221112895
## 818 0.6174261485 0.00147914278860 0.000776268989 0.332464958 0.03112393126
## 819 0.2026047851 0.00004519989536 0.016754076784 0.568126183 0.00817025418
## 820 0.6174261485 0.00147914278860 0.000776268989 0.332464958 0.03112393126
## 821 0.0308005707 0.00000035882528 0.060430738234 0.320348164 0.00059222752
## 822 0.0352263627 0.00001532949156 0.112090147034 0.361422378 0.00453267588
## 823 0.0627494852 0.00000376560052 0.050415703003 0.432346888 0.00221445833
## 824 0.6548386204 0.01298841232411 0.000407030493 0.244552121 0.08022422746
## 825 0.0283417464 0.00000167823790 0.087380442143 0.319220627 0.00134954242
## 826 0.0526421235 0.00000378274325 0.060029228776 0.406065406 0.00220485609
## 827 0.1811625411 0.00009424590520 0.023014451594 0.567904793 0.01224638339
## 828 0.2810098858 0.00003541043616 0.008345877691 0.558491023 0.00676684376
## 829 0.0517352078 0.00000888572892 0.071773450433 0.409286680 0.00348567849
## 830 0.5165202415 0.00008055826287 0.001469181568 0.430325052 0.00810881888
## 831 0.5165202415 0.00008055826287 0.001469181568 0.430325052 0.00810881888
## 832 0.2503571948 0.00046908788780 0.015827801243 0.568200624 0.02722654788
## 833 0.4579008720 0.02123940535145 0.002589498863 0.365030742 0.13110419874
## 834 0.4170360685 0.02856890458579 0.003074099899 0.362218606 0.16608183588
## 835 0.1767048420 0.00001778316674 0.018045105129 0.557349518 0.00504340659
## 836 0.1343440431 0.00001978368242 0.027522285911 0.540541455 0.00545067254
## 837 0.7358164230 0.00394806254859 0.000195841856 0.215867255 0.03888345029
## 838 0.3989008522 0.00120695267894 0.005126861778 0.496256239 0.03832190555
## 839 0.3989008522 0.00120695267894 0.005126861778 0.496256239 0.03832190555
## 840 0.6606718562 0.10341897359237 0.000053838147 0.105368794 0.12946290542
## 841 0.0351373918 0.00000456699491 0.088868312194 0.350581889 0.00236563942
## 842 0.5242556914 0.02842926149559 0.001133827486 0.293706209 0.14055350691
## 843 0.0181122718 0.00000042035996 0.093087199957 0.258235164 0.00060977924
## 844 0.7292669847 0.01007813337987 0.000178277510 0.195825435 0.06054632964
## 845 0.5698602468 0.00020119084049 0.001039626660 0.387387448 0.01211925186
## 846 0.5998182625 0.00101316963013 0.000909811231 0.351685205 0.02656133912
## 847 0.1412613938 0.00000505811749 0.020260628599 0.530967932 0.00261373334
## 848 0.6668123692 0.00134946771792 0.000468762075 0.292227859 0.02712684593
## 849 0.5998182625 0.00101316963013 0.000909811231 0.351685205 0.02656133912
## 850 0.1392519381 0.00001200464460 0.024165107114 0.538339584 0.00414890320
## 851 0.1099800375 0.00002161651851 0.036867075053 0.518504080 0.00572924478
## 852 0.7408858357 0.00988501060941 0.000149918094 0.187634575 0.05780047600
## 853 0.1828258871 0.00002076375390 0.017495972336 0.560783319 0.00545372830
## 854 0.1828258871 0.00002076375390 0.017495972336 0.560783319 0.00545372830
## 855 0.6367353933 0.00591302602234 0.000597607363 0.287263413 0.05878004386
## 856 0.1828258871 0.00002076375390 0.017495972336 0.560783319 0.00545372830
## 857 0.1668583498 0.00002574204412 0.021085625806 0.557322121 0.00617010792
## 858 0.5195549770 0.00195709188790 0.001935329385 0.405182337 0.04197383328
## 859 0.5195549770 0.00195709188790 0.001935329385 0.405182337 0.04197383328
## 860 0.2801353452 0.00002539626819 0.007965113672 0.557032822 0.00567540183
## 861 0.6612985113 0.00119134544910 0.000495472750 0.298467330 0.02576502354
## 862 0.2204736522 0.00003618493115 0.013809539865 0.567638421 0.00717030217
## 863 0.0913535921 0.00000579973920 0.036511002630 0.484646240 0.00283359020
## 864 0.0913535921 0.00000579973920 0.036511002630 0.484646240 0.00283359020
## 865 0.3491690759 0.00006519255436 0.005364808019 0.534766351 0.00879145342
## 866 0.6116725927 0.00031300217148 0.000734049528 0.351636603 0.01421813931
## 867 0.7199942099 0.00155550704984 0.000252056252 0.244834265 0.02580114559
## 868 0.6462696688 0.00128058569068 0.000577560123 0.310409294 0.02744427962
## 869 0.0067715313 0.00000088598264 0.200377823854 0.167137900 0.00079905600
## 870 0.5681677421 0.00020059922250 0.001043277609 0.389025812 0.01210946596
## 871 0.0303107192 0.00000034555845 0.060402555434 0.314133526 0.00058087182
## 872 0.5135058456 0.01224526856989 0.001727898325 0.352264725 0.10121440116
## 873 0.5681677421 0.00020059922250 0.001043277609 0.389025812 0.01210946596
## 874 0.1144657896 0.00084104177707 0.063742831805 0.527532730 0.04017204292
## 875 0.6695650868 0.00097661983859 0.000450290692 0.293668555 0.02292674895
## 876 0.1144657896 0.00084104177707 0.063742831805 0.527532730 0.04017204292
## 877 0.7099457032 0.00805697499044 0.000244446243 0.219740360 0.05663103578
## 878 0.2301186408 0.00081018727576 0.020140373117 0.571122271 0.03376000890
## 879 0.7165006364 0.00214775902519 0.000262046811 0.243281960 0.03049596466
## 880 0.6545758117 0.00105010547965 0.000525552752 0.305784862 0.02443678692
## 881 0.4265138597 0.00049203050984 0.003793812189 0.487463575 0.02337273075
## 882 0.6436956358 0.00754844543940 0.000532212707 0.273989320 0.06482042816
## 883 0.3716581687 0.00070377075350 0.006137556338 0.517614221 0.02994614443
## 884 0.6054582698 0.00114931440492 0.000864247461 0.345662386 0.02799505164
## 885 0.6728665618 0.00652099542050 0.000395144345 0.255307606 0.05703708240
## 886 0.7031756429 0.00361274780443 0.000299139844 0.245199359 0.04044727787
## 887 0.4379257669 0.00266742141150 0.003852160548 0.457508990 0.05458058892
## 888 0.7245318453 0.00175854198026 0.000238019537 0.239266190 0.02711023168
## 889 0.3347910386 0.00049181025088 0.007933346850 0.539111428 0.02582267535
## 890 0.3117177494 0.00086396427998 0.010193767000 0.544990924 0.03542588577
## 891 0.7267892910 0.00291460078207 0.000226757871 0.229378268 0.03446647693
## 892 0.4399812156 0.10526785019179 0.001021587772 0.232497265 0.21328663983
## 893 0.5143926005 0.00021499985762 0.001678168533 0.430241196 0.01357951469
## 894 0.0788149052 0.00000841520531 0.046325293454 0.468336451 0.00345146146
## 895 0.4880108975 0.00074072113512 0.002391725071 0.441751451 0.02674459104
## 896 0.4880108975 0.00074072113512 0.002391725071 0.441751451 0.02674459104
## 897 0.1573684843 0.00003697618080 0.024669361065 0.555353636 0.00753157841
## 898 0.5550280023 0.00015519137094 0.001149588719 0.399912013 0.01082405502
## 899 0.0023690203 0.00000017281948 0.246681296035 0.093064040 0.00028042679
## 900 0.1190178867 0.00002944919454 0.035398762171 0.528553384 0.00676147744
## 901 0.2085734419 0.00007266387127 0.017025425905 0.571911401 0.01048893806
## 902 0.7026213943 0.00148539656736 0.000310952068 0.260465509 0.02626893423
## 903 0.2914372306 0.00041032731416 0.011032408273 0.557722763 0.02448417653
## 904 0.7227320337 0.00703990062991 0.000214262955 0.212286816 0.05269661405
## 905 0.0694166191 0.00000985377464 0.054520784353 0.454652929 0.00302722923
## 906 0.6813627314 0.00474963761681 0.000379882286 0.256803033 0.04855529810
## 907 0.7227320337 0.00703990062991 0.000214262955 0.212286816 0.05269661405
## 908 0.0838551103 0.00000425132274 0.037877056135 0.472468172 0.00238808720
## 909 0.3809967088 0.00015744489122 0.004744641065 0.518949758 0.01355048001
## 910 0.3809967088 0.00015744489122 0.004744641065 0.518949758 0.01355048001
## 911 0.4761541728 0.00616005261524 0.002752994780 0.406835272 0.07834749518
## 912 0.5510557143 0.00171661475874 0.001458645559 0.383316094 0.03749706790
## 913 0.2398025233 0.00005583308522 0.012482182894 0.569506122 0.00891599450
## 914 0.2870930232 0.00004061031526 0.008048853471 0.558305082 0.00724609604
## 915 0.5312622144 0.00116873247034 0.001704111269 0.404371055 0.03176660597
## 916 0.5312622144 0.00116873247034 0.001704111269 0.404371055 0.03176660597
## 917 0.4389613557 0.00417852174942 0.003794567266 0.445519081 0.06800240335
## 918 0.3965493717 0.00112829441885 0.005243633436 0.498175267 0.03716752062
## 919 0.0396115288 0.00000496440316 0.081653551119 0.367755162 0.00249985349
## 920 0.6905902669 0.00250203268880 0.000355447943 0.262861728 0.03488618548
## 921 0.1084338810 0.00007037676765 0.046614767320 0.522499436 0.01079418827
## 922 0.1269263567 0.00018257414703 0.044287865313 0.542703444 0.01786303740
## 923 0.1010179874 0.27574332845992 0.000003176878 0.007786644 0.61542937071
## 924 0.1081460746 0.00024709029334 0.057568520134 0.524734782 0.02106895866
## 925 0.3539098256 0.01180024409127 0.007312121468 0.454177577 0.12431528983
## 926 0.1554726768 0.00001892883502 0.022338505643 0.549950651 0.00526988463
## 927 0.7687991338 0.01922095757546 0.000070702760 0.141540101 0.06851921762
## 928 0.6231587481 0.00167711597981 0.000735306528 0.325885352 0.03279999509
## 929 0.6045392489 0.00387412752801 0.000867403742 0.324548736 0.05113208087
## 930 0.7370689976 0.00253675331652 0.000200088828 0.222946418 0.03139890267
## 931 0.0400864693 0.00001770683362 0.102256410294 0.375437146 0.00495047341
## 932 0.2502887758 0.48254393578695 0.000040735532 0.045845925 0.22086993100
## 933 0.6349248546 0.00215484886977 0.000657130304 0.311930997 0.03643225158
## 934 0.1509278246 0.00104817360616 0.044022692730 0.552793797 0.04446210456
## 935 0.7837999942 0.01409002591614 0.000067521993 0.141576654 0.05855946383
## 936 0.1509278246 0.00104817360616 0.044022692730 0.552793797 0.04446210456
## 937 0.1100215523 0.00000901786073 0.031112456317 0.514149034 0.00359910605
## 938 0.3348594587 0.00013258261146 0.006660790894 0.542319540 0.01296978102
## 939 0.3348594587 0.00013258261146 0.006660790894 0.542319540 0.01296978102
## 940 0.3348594587 0.00013258261146 0.006660790894 0.542319540 0.01296978102
## 941 0.3348594587 0.00013258261146 0.006660790894 0.542319540 0.01296978102
## 942 0.6703506540 0.00070491216001 0.000434459201 0.296309721 0.01934372884
## 943 0.6181040590 0.00147989457743 0.000774535606 0.331777826 0.03115697150
## 944 0.6181040590 0.00147989457743 0.000774535606 0.331777826 0.03115697150
## 945 0.7476547229 0.04773871215447 0.000041229027 0.108975569 0.09456592159
## 946 0.4924063821 0.00010448780910 0.001852508213 0.447700050 0.00959317209
## 947 0.7164199430 0.01377390822664 0.000185571515 0.194798532 0.07085706423
## 948 0.6583376793 0.02344184098828 0.000297947342 0.212615798 0.10035640451
## 949 0.0733783035 0.00007676686627 0.074706126929 0.470240029 0.01125840117
## 950 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 951 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 952 0.6174377757 0.00049270979272 0.000725955757 0.343973278 0.01779408383
## 953 0.3964228514 0.00028695272113 0.004542919503 0.508764639 0.01825991514
## 954 0.3964228514 0.00028695272113 0.004542919503 0.508764639 0.01825991514
## 955 0.3964228514 0.00028695272113 0.004542919503 0.508764639 0.01825991514
## 956 0.3964228514 0.00028695272113 0.004542919503 0.508764639 0.01825991514
## 957 0.2201007672 0.00005016083823 0.014493083747 0.570421341 0.00854453583
## 958 0.4124296779 0.00014091670981 0.003650016961 0.500722029 0.01235348394
## 959 0.2553493375 0.00038872944278 0.014602519997 0.569108156 0.02455130175
## 960 0.7012098966 0.00339886016013 0.000307928506 0.248109298 0.03947024934
## 961 0.2003393915 0.00039591384078 0.024035993610 0.571853944 0.02587837137
## 962 0.5378202668 0.00195597319838 0.001639561693 0.391467000 0.04077632238
## 963 0.3376656786 0.00002900153307 0.005108747820 0.538574856 0.00578847438
## 964 0.3540169614 0.00003842507733 0.004710481177 0.531935802 0.00661494944
## 965 0.2090508991 0.00005240867041 0.016222691056 0.569339807 0.00880270565
## 966 0.6949925948 0.00282886535362 0.000335702561 0.256920825 0.03666089215
## 967 0.4794871989 0.00025517935372 0.002299402557 0.454846202 0.01556083723
## 968 0.6421192443 0.02668866076397 0.000334179453 0.216535938 0.10910834613
## 969 0.4794871989 0.00025517935372 0.002299402557 0.454846202 0.01556083723
## 970 0.1914935152 0.00031772036963 0.025266835486 0.571294575 0.02318057985
## 971 0.6787189373 0.00830613869713 0.000351077243 0.242961701 0.06275862954
## 972 0.4192822155 0.00043023269762 0.003971810098 0.492844067 0.02198344646
## 973 0.6873282461 0.01191289980897 0.000290254654 0.222602768 0.07226115802
## 974 0.7693487963 0.01711181560769 0.000075415066 0.145836136 0.06564174562
## 975 0.2120910079 0.00027432356490 0.020505837099 0.573127537 0.02114623804
## 976 0.7693487963 0.01711181560769 0.000075415066 0.145836136 0.06564174562
## 977 0.6498599993 0.04160542232258 0.000214049499 0.181504193 0.12337575919
## 978 0.4917567614 0.00020126426199 0.002021621727 0.446879168 0.01353157809
## 979 0.3724430850 0.00050818998296 0.005887267839 0.519639375 0.02526144698
## 980 0.3724430850 0.00050818998296 0.005887267839 0.519639375 0.02526144698
## 981 0.5966179210 0.00139905192518 0.000948001991 0.350272453 0.03138423664
## 982 0.5966179210 0.00139905192518 0.000948001991 0.350272453 0.03138423664
## 983 0.7287006267 0.01133405670351 0.000169500852 0.192613946 0.06331267801
## 984 0.7664661248 0.02421597968262 0.000062038485 0.133109036 0.07454466690
## 985 0.6770867365 0.00269716824878 0.000413413917 0.272888125 0.03726590725
## 986 0.5347425502 0.00183478270671 0.001682625108 0.394943450 0.03967501809
## 987 0.0552944470 0.00002062714224 0.078743728213 0.423503527 0.00549930313
## 988 0.3762288431 0.00111493881654 0.006116000419 0.510694023 0.03774396363
## 989 0.6287149590 0.00190091008401 0.000696256394 0.319323412 0.03455423922
## 990 0.4742321506 0.00041118489826 0.002527561094 0.456289133 0.02007905965
## 991 0.7054169706 0.00479229498866 0.000285224003 0.236708838 0.04614801396
## 992 0.7003399718 0.00423906043809 0.000304544620 0.244071679 0.04391127329
## 993 0.5904887372 0.00123257216116 0.000999721420 0.356960616 0.02974872002
## 994 0.3757488928 0.00154035986129 0.006356048761 0.505938924 0.04462990288
## 995 0.6434800202 0.00430288364012 0.000577816346 0.290532835 0.04997096979
## 996 0.0494921616 0.00000227324755 0.057620843015 0.393590596 0.00166958210
## 997 0.3718645978 0.00003686575627 0.002979727082 0.523487197 0.00635370548
## 998 0.3426930832 0.00021157818120 0.006683462486 0.538396425 0.01646362355
## 999 0.1307340239 0.00000369995797 0.021309174500 0.520836234 0.00221720470
## 1000 0.3502733301 0.00024294601840 0.006411277296 0.534439144 0.01757032823
## 1001 0.0227288347 0.00000084293454 0.090452380144 0.287992721 0.00091012889
## 1002 0.3502733301 0.00024294601840 0.006411277296 0.534439144 0.01757032823
## 1003 0.3399572834 0.03536781465378 0.005942899891 0.383492758 0.20216735639
## 1004 0.3425294548 0.00015236778923 0.006394389175 0.538780038 0.01385257329
## 1005 0.1307340239 0.00000369995797 0.021309174500 0.520836234 0.00221720470
## 1006 0.5334481999 0.00084547884985 0.001637670230 0.406504612 0.02684886389
## 1007 0.4570095766 0.00004369778925 0.002142863156 0.472803447 0.00632614460
## 1008 0.6845287021 0.00141616688720 0.000382939298 0.276637146 0.02670158496
## 1009 0.4570095766 0.00004369778925 0.002142863156 0.472803447 0.00632614460
## 1010 0.0461120760 0.00000327520002 0.065931174079 0.388619951 0.00201677975
## 1011 0.5728936026 0.00060779015038 0.001123409739 0.378681426 0.02134928152
## 1012 0.6194190804 0.00231371311171 0.000763691848 0.323560954 0.03873500699
## 1013 0.5728936026 0.00060779015038 0.001123409739 0.378681426 0.02134928152
## 1014 0.5682798972 0.00014446755737 0.000999803861 0.390022324 0.01019894454
## 1015 0.2240520083 0.00018976662284 0.017466656677 0.573011754 0.01724960108
## 1016 0.2986695088 0.00024409540935 0.009715930225 0.557515061 0.01758222020
## 1017 0.1772164979 0.00003442586167 0.019921822393 0.563630183 0.00718542344
## 1018 0.3762288431 0.00111493881654 0.006116000419 0.510694023 0.03774396363
## 1019 0.0598025272 0.00000076059166 0.037877322428 0.415808117 0.00093959786
## 1020 0.3408064781 0.00007858232681 0.005849711331 0.539519678 0.00978881999
## 1021 0.4761492767 0.00015437172452 0.002222349014 0.458658175 0.01201620535
## 1022 0.4045626448 0.00008867395666 0.003638955900 0.506727123 0.00977213042
## 1023 0.7369574767 0.00104290459219 0.000205573535 0.234368475 0.02036410195
## 1024 0.7360075782 0.00017976101638 0.000178094237 0.246525577 0.00825996462
## 1025 0.4706216644 0.03420636371620 0.001708109213 0.314230428 0.16422773339
## 1026 0.5018501961 0.00096211962893 0.002167777880 0.428917541 0.02999657055
## 1027 0.3831478526 0.00158174793479 0.005994185786 0.501267385 0.04486413923
## 1028 0.1920606786 0.00024446652662 0.023759930233 0.573756900 0.02016519773
## 1029 0.7194972762 0.01551712066729 0.000172120800 0.187020173 0.07413220035
## 1030 0.6720476062 0.00371565692204 0.000428755448 0.270553299 0.04394830815
## 1031 0.1455885031 0.00001957367508 0.023924592816 0.546043973 0.00538166365
## 1032 0.1402133547 0.00001677795192 0.025421373408 0.541365714 0.00496283905
## 1033 0.1591490397 0.00001134406074 0.019627853591 0.548711796 0.00399348844
## 1034 0.2123088479 0.00027391653629 0.020484645115 0.574714087 0.02114444791
## 1035 0.2621104457 0.00008661344788 0.011213937237 0.566504222 0.01104954686
## 1036 0.5114925525 0.03213418630103 0.001245952054 0.291313933 0.15150612979
## 1037 0.7092597566 0.00056119060234 0.000275407104 0.264324497 0.01583201542
## 1038 0.2277737947 0.00001262600688 0.010685465118 0.562664188 0.00407333195
## 1039 0.0295726384 0.00000042627119 0.064574862066 0.316276120 0.00064731702
## 1040 0.3496290846 0.00009066237714 0.005612328646 0.535124133 0.01046028945
## 1041 0.6560875235 0.01296419894013 0.000406789351 0.244354469 0.07919771792
## 1042 0.4157425542 0.00113993777368 0.004489771401 0.486173465 0.03653937869
## 1043 0.6319941404 0.00137692969231 0.000669908990 0.321397624 0.02923991042
## 1044 0.4914877920 0.00027884676613 0.002102078422 0.446754094 0.01607103765
## 1045 0.4483995754 0.14145726787865 0.000572027054 0.187706381 0.21698281158
## 1046 0.4914877920 0.00027884676613 0.002102078422 0.446754094 0.01607103765
## 1047 0.6225982002 0.12001558207688 0.000055511597 0.101126307 0.15522074542
## 1048 0.6799611058 0.00474580515972 0.000382097081 0.258232330 0.04849284273
## 1049 0.1151085986 0.00049749618855 0.056672321091 0.538383249 0.03040601004
## 1050 0.1601058340 0.00001576641301 0.020668878446 0.550613980 0.00476875327
## 1051 0.3481798435 0.00064732662560 0.007290649759 0.531156083 0.02937681030
## 1052 0.7485010974 0.00069496363034 0.000170559133 0.227936217 0.01602653251
## 1053 0.0097358421 0.00000230572045 0.193455065452 0.201376422 0.00140698775
## 1054 0.7485010974 0.00069496363034 0.000170559133 0.227936217 0.01602653251
## 1055 0.0091950078 0.00000216226202 0.197770502136 0.195849538 0.00134830564
## 1056 0.6268121261 0.03154388097463 0.000350715644 0.213327591 0.12285318782
## 1057 0.2016004149 0.00000972445294 0.012760792874 0.559938901 0.00360487254
## 1058 0.5663103873 0.00160544920515 0.001265852451 0.372779148 0.03506316123
## 1059 0.2016004149 0.00000972445294 0.012760792874 0.559938901 0.00360487254
## 1060 0.2166410304 0.00001838129558 0.012561193743 0.565052662 0.00501615279
## 1061 0.6655154138 0.00308978283266 0.000466987740 0.279908093 0.04078356730
## 1062 0.5580963799 0.00305136557379 0.001363213429 0.366843952 0.04924174918
## 1063 0.4673667577 0.00150552952561 0.002970911382 0.447842806 0.03944559453
## 1064 0.2225417142 0.00018905153292 0.017479067720 0.574322453 0.01723789637
## 1065 0.0148620392 0.00000023703987 0.094536756826 0.230795145 0.00043825034
## 1066 0.1886690460 0.00051699090476 0.027084434326 0.572695401 0.02996543120
## 1067 0.6012785816 0.00073215393605 0.000875560172 0.354004382 0.02241915670
## 1068 0.3556957770 0.00002793563616 0.004472762933 0.531231481 0.00557582943
## 1069 0.3119428538 0.00008707785219 0.007506517327 0.551406288 0.01061294038
## 1070 0.3340028248 0.00284836561578 0.009254173090 0.516123489 0.06379325939
## 1071 0.3340028248 0.00284836561578 0.009254173090 0.516123489 0.06379325939
## 1072 0.7418010008 0.02521543809031 0.000090173919 0.148785512 0.08202762780
## 1073 0.2348900508 0.00006761015314 0.013533783436 0.569884785 0.00989640791
## 1074 0.1308915969 0.00001714941926 0.028207437630 0.534545598 0.00504571824
## 1075 0.6597826771 0.00830357040812 0.000425896711 0.253080316 0.07077019406
## 1076 0.5153761482 0.01151647144745 0.001767640738 0.353007369 0.09878188015
## 1077 0.1308915969 0.00001714941926 0.028207437630 0.534545598 0.00504571824
## 1078 0.1974188527 0.00000848561219 0.013143989824 0.555752469 0.00335658372
## 1079 0.0777503426 0.00000103396591 0.030674160317 0.453236142 0.00112027550
## 1080 0.4684086368 0.00007002018014 0.002131700428 0.464638270 0.00801219005
## 1081 0.6620431270 0.00452267529563 0.000470288642 0.274140422 0.04926776388
## 1082 0.7588850263 0.03376334724561 0.000053383498 0.121041124 0.08492437460
## 1083 0.7571324740 0.03356075209026 0.000055408494 0.122548553 0.08533179606
## 1084 0.4585611114 0.00043714291566 0.002897411803 0.466770305 0.02114509383
## 1085 0.3649803001 0.00016583582060 0.005399645815 0.527758985 0.01416811917
## 1086 0.5891349147 0.00415327818366 0.001002978001 0.335009510 0.05431396218
## 1087 0.2292309786 0.00001767715205 0.011207775379 0.564635920 0.00486987956
## 1088 0.3961975557 0.00020662050617 0.004350775657 0.509997803 0.01538237025
## 1089 0.5598509995 0.00033906771814 0.001193951057 0.393156043 0.01611697062
## 1090 0.5761590174 0.00031733217525 0.001031486530 0.380350495 0.01520631942
## 1091 0.3161195923 0.00001912941221 0.005737117215 0.544073376 0.00473474367
## 1092 0.5853513255 0.00366480706068 0.001056981589 0.340919112 0.05161916983
## 1093 0.1795865778 0.00004845550429 0.020896881097 0.564586233 0.00859960657
## 1094 0.5914957791 0.00442020017888 0.000975617582 0.331494141 0.05572820291
## 1095 0.5510697642 0.00057220423244 0.001363168205 0.396382101 0.02142605363
## 1096 0.6477414923 0.00015967106219 0.000475768063 0.324249293 0.00939077243
## 1097 0.1825694091 0.00018280120703 0.025280915954 0.571346355 0.01740642733
## 1098 0.6084268917 0.00083208849447 0.000828667324 0.346757605 0.02368086375
## 1099 0.1746723259 0.00042149031369 0.030074070775 0.570588700 0.02715663761
## 1100 0.1759843569 0.00011313472144 0.024896763496 0.567155626 0.01353747945
## 1101 0.1554793568 0.00001900689725 0.022305216334 0.550587122 0.00527358190
## 1102 0.5727332179 0.00182268548633 0.001199021276 0.365268604 0.03734605502
## 1103 0.5107391822 0.00079245781841 0.001981623558 0.424403964 0.02682891198
## 1104 0.3328841638 0.00006829963158 0.006091131224 0.542842137 0.00915842738
## 1105 0.1212133712 0.00001263983248 0.028723823443 0.525479630 0.00428851255
## 1106 0.7233484291 0.00426691616831 0.000228423566 0.224698970 0.04164511327
## 1107 0.3386306761 0.00149553914119 0.008590878401 0.526067886 0.04575736594
## 1108 0.7400812130 0.00931456814260 0.000154595580 0.190183547 0.05649589976
## 1109 0.4229228400 0.00053998325971 0.003958429791 0.489773157 0.02468861503
## 1110 0.6647727273 0.04099488810713 0.000178668790 0.172721955 0.11829330690
## 1111 0.4888571254 0.01774173937290 0.002010899605 0.347786116 0.12419345020
## 1112 0.6905902669 0.00250203268880 0.000355447943 0.262861728 0.03488618548
## 1113 0.5966179210 0.00139905192518 0.000948001991 0.350272453 0.03138423664
## 1114 0.5966179210 0.00139905192518 0.000948001991 0.350272453 0.03138423664
## 1115 0.5966179210 0.00139905192518 0.000948001991 0.350272453 0.03138423664
## 1116 0.6068940514 0.00412257329744 0.000843635092 0.321104502 0.05245686191
## 1117 0.6068940514 0.00412257329744 0.000843635092 0.321104502 0.05245686191
## 1118 0.4204145021 0.00179764036749 0.004450691015 0.476288009 0.04584114413
## 1119 0.0639059215 0.00000065819813 0.035238232963 0.416983643 0.00086864510
## 1120 0.6068940514 0.00412257329744 0.000843635092 0.321104502 0.05245686191
## 1121 0.7104089288 0.00121604549653 0.000282427275 0.256156302 0.02335077403
## 1122 0.1871207038 0.00010937804903 0.022309216764 0.569646353 0.01320778116
## 1123 0.0329441279 0.00000273791751 0.084972838125 0.338958689 0.00178620877
## 1124 0.1777237110 0.00002485447245 0.018953785096 0.560028713 0.00602797980
## 1125 0.7137782695 0.00520141139692 0.000250965960 0.228033929 0.04675798678
## 1126 0.0831134102 0.00008881846980 0.066805544973 0.488484713 0.01221112895
## 1127 0.1444017636 0.00008893881973 0.032669895971 0.553039741 0.01210188396
## 1128 0.1806486228 0.00001057223779 0.015875425656 0.554286346 0.00381119017
## 1129 0.2298483024 0.00113111330558 0.021005949442 0.564683532 0.04384742459
## 1130 0.3319850977 0.00130495075886 0.008974330926 0.531221813 0.04295877367
## 1131 0.0632019783 0.00000522157210 0.053414090614 0.434184040 0.00264875739
## 1132 0.2349122541 0.00139133808309 0.019878985749 0.564705416 0.04844124951
## 1133 0.5220612033 0.00024516765009 0.001598055129 0.423965655 0.01438861324
## 1134 0.0809942946 0.00000507490389 0.040724094326 0.469290988 0.00262465030
## 1135 0.0986093100 0.00000569148386 0.033217773086 0.493984026 0.00280642354
## 1136 0.3788500971 0.00080589065214 0.005871494055 0.512327234 0.03187306600
## 1137 0.1937176668 0.00017665147718 0.022655035245 0.571706143 0.01696489242
## 1138 0.3131928162 0.00261617726107 0.010974119767 0.527570779 0.06245139260
## 1139 0.2841166932 0.00018482668641 0.010497098385 0.561768605 0.01619730405
## 1140 0.6871925948 0.00221474687500 0.000374290956 0.267642009 0.03322956139
## 1141 0.4628991800 0.00095575439990 0.002998546610 0.457024943 0.03147838837
## 1142 0.2704938205 0.00026859602386 0.012386110118 0.565310124 0.01996254855
## 1143 0.5595763080 0.00207857876699 0.001349589221 0.373489827 0.04064701233
## Very Good
## 1 0.00476915808
## 2 0.00258150823
## 3 0.00604226893
## 4 0.09635468413
## 5 0.00476915808
## 6 0.00620388676
## 7 0.00911295268
## 8 0.01587224536
## 9 0.01141712419
## 10 0.00756281662
## 11 0.01770721650
## 12 0.00531652806
## 13 0.19976510331
## 14 0.03590591085
## 15 0.04841812547
## 16 0.02945310355
## 17 0.01792665194
## 18 0.04579619456
## 19 0.02788914119
## 20 0.03300392327
## 21 0.00446160059
## 22 0.01266208618
## 23 0.01649650393
## 24 0.01192405595
## 25 0.03608940053
## 26 0.00931465116
## 27 0.06545040210
## 28 0.04948384953
## 29 0.09641492737
## 30 0.00731702471
## 31 0.07929202174
## 32 0.02411504677
## 33 0.51919904343
## 34 0.00060597967
## 35 0.00450231441
## 36 0.01491920654
## 37 0.01683911067
## 38 0.03400629357
## 39 0.04903651117
## 40 0.03584694635
## 41 0.02375775511
## 42 0.03203962148
## 43 0.02277371981
## 44 0.00433819325
## 45 0.00713521759
## 46 0.03773009647
## 47 0.03773009647
## 48 0.03232679722
## 49 0.02444225863
## 50 0.00597743760
## 51 0.00407947964
## 52 0.11632746901
## 53 0.01986334866
## 54 0.01027873646
## 55 0.00482364082
## 56 0.01691686169
## 57 0.01069867890
## 58 0.15306067116
## 59 0.03440404938
## 60 0.03659669674
## 61 0.02401709688
## 62 0.03693390422
## 63 0.00423598550
## 64 0.01737079855
## 65 0.03659669674
## 66 0.02401709688
## 67 0.00259925074
## 68 0.02123985533
## 69 0.00221509071
## 70 0.00683922271
## 71 0.02804894660
## 72 0.00683922271
## 73 0.00780726551
## 74 0.01792665194
## 75 0.00780726551
## 76 0.02483698804
## 77 0.00642921878
## 78 0.01362688144
## 79 0.01481022375
## 80 0.07352545722
## 81 0.01362688144
## 82 0.12167812267
## 83 0.03170430303
## 84 0.00011095727
## 85 0.09461686285
## 86 0.00356623635
## 87 0.01957619698
## 88 0.00008140059
## 89 0.00008140059
## 90 0.04622293332
## 91 0.00352201922
## 92 0.52066935418
## 93 0.00073798379
## 94 0.00486400158
## 95 0.02084602075
## 96 0.00486400158
## 97 0.67192243553
## 98 0.03213377515
## 99 0.67192243553
## 100 0.00581214756
## 101 0.01378806398
## 102 0.05455652171
## 103 0.13420760313
## 104 0.01496902758
## 105 0.02583375478
## 106 0.02583375478
## 107 0.10600545467
## 108 0.10600545467
## 109 0.10600545467
## 110 0.00463600548
## 111 0.00775671503
## 112 0.00105686867
## 113 0.00394594138
## 114 0.03355194497
## 115 0.00911295268
## 116 0.00970550227
## 117 0.00880433923
## 118 0.02354570906
## 119 0.00666978320
## 120 0.03298619137
## 121 0.00077223111
## 122 0.02015333124
## 123 0.13196090030
## 124 0.01957619698
## 125 0.03905130958
## 126 0.06816618126
## 127 0.00321674193
## 128 0.00999047569
## 129 0.00999047569
## 130 0.00731163308
## 131 0.00331418547
## 132 0.00731163308
## 133 0.00682084056
## 134 0.07369622809
## 135 0.02414816071
## 136 0.01957619698
## 137 0.01688218592
## 138 0.06125666534
## 139 0.01949679291
## 140 0.01949679291
## 141 0.03331963652
## 142 0.12357315439
## 143 0.00582790169
## 144 0.13827612625
## 145 0.01899081472
## 146 0.24348537419
## 147 0.24348537419
## 148 0.02409352995
## 149 0.37648057032
## 150 0.01032935589
## 151 0.03071517549
## 152 0.00709378304
## 153 0.07931107918
## 154 0.07353548467
## 155 0.00246742054
## 156 0.02770644634
## 157 0.04236633099
## 158 0.01407494250
## 159 0.00999047569
## 160 0.01707668721
## 161 0.10594427074
## 162 0.01128940630
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## 164 0.06605099038
## 165 0.04713358460
## 166 0.00022586313
## 167 0.00392936009
## 168 0.00622460563
## 169 0.19853361866
## 170 0.01885046387
## 171 0.06120630007
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## 173 0.01341052987
## 174 0.00638158499
## 175 0.01341052987
## 176 0.10047715172
## 177 0.01935918665
## 178 0.15192393775
## 179 0.00337168021
## 180 0.00942306091
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## 183 0.02866471049
## 184 0.17748714035
## 185 0.03919054210
## 186 0.00061104960
## 187 0.03549999979
## 188 0.03127781691
## 189 0.09020989828
## 190 0.00279108234
## 191 0.61482664377
## 192 0.01320167476
## 193 0.20626734169
## 194 0.07665263949
## 195 0.07665263949
## 196 0.01320167476
## 197 0.20626734169
## 198 0.60936053075
## 199 0.12071028820
## 200 0.05711779502
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## 231 0.01321505243
## 232 0.01321505243
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## 234 0.26732425941
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## 239 0.49485047455
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## 266 0.16479072662
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## 288 0.34846496041
## 289 0.01737079855
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##
## $terms
## quality ~ alcohol + `volatile acidity`
## attr(,"variables")
## list(quality, alcohol, `volatile acidity`)
## attr(,"factors")
## alcohol `volatile acidity`
## quality 0 0
## alcohol 1 0
## `volatile acidity` 0 1
## attr(,"term.labels")
## [1] "alcohol" "`volatile acidity`"
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(quality, alcohol, `volatile acidity`)
## attr(,"dataClasses")
## quality alcohol volatile acidity
## "character" "numeric" "numeric"
##
## $call
## lda(formula = quality ~ ., data = df, CV = TRUE)
##
## $xlevels
## named list()attach(df)## The following objects are masked from df (pos = 8):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 9):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 10):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 11):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 23):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 26):
##
## alcohol, quality, volatile acidityhead(r2$class)## [1] Average Average Average Good Average Average
## Levels: Average Bad Excellent Good Poor Very Good#the Maximum a Posteriori Probability (MAP) classification (a factor)
#posterior: posterior probabilities for the classes.
head(r2$posterior, 3)## Average Bad Excellent Good Poor Very Good
## 1 0.7324358 0.006128663 0.0001911400 0.2084485 0.04802675 0.004769158
## 2 0.6776194 0.042690611 0.0001439515 0.1614247 0.11553985 0.002581508
## 3 0.6833591 0.010562113 0.0003113378 0.2307911 0.06893412 0.006042269train <- sample(1:150, 75)
train## [1] 70 120 135 63 60 54 89 84 5 95 48 10 15 51 117 126 21 4 47
## [20] 136 141 59 9 96 53 97 33 91 8 85 113 35 28 82 24 127 145 37
## [39] 133 115 104 16 30 112 139 25 19 148 144 116 57 110 40 50 14 125 42
## [58] 12 83 76 132 44 6 2 92 123 130 18 114 29 11 32 72 26 100# Assuming `train` is a vector of indices
train_indices <- train
train_df <- df[train_indices, ]
# Calculate prior probabilities
unique_classes <- unique(train_df$quality)
num_classes <- length(unique_classes)
prior <- rep(1/num_classes, num_classes)
# Train the LDA model
r3 <- lda(quality ~ .,
train_df,
prior = prior)Model Insights (10 points)
Prediction
Observation 1 has a high LD1 value and a moderate LD2 value, suggesting a higher probability of belonging to the “Average” quality class.
Observation 2 has a moderate LD1 value and a negative LD2 value, suggesting a higher probability of belonging to the “Poor” quality class.
Observation 3 has a negative LD1 value and a high LD2 value, indicating a higher probability of belonging to the “Very Good” quality class.
Observation 4 has a moderate LD1 value and a moderate LD2 value, suggesting a higher probability of belonging to the “Average” quality class.
Observation 5 has a high LD1 value and a negative LD2 value, indicating a higher probability of belonging to the “Average” quality class.
Observation 6 has a negative LD1 value and a positive LD2 value, suggesting a higher probability of belonging to the “Good” quality class.
plda = predict(object = r3, # predictions
newdata = df[-train, ])
head(plda$class)## [1] Average Average Average Very Good Very Good Very Good
## Levels: Average Good Poor Very Goodhead(plda$posterior, 6) # posterior prob.## Average Good Poor Very Good
## 1 0.38216170 0.1633311 0.2892684 0.1652388
## 2 0.37850895 0.1566560 0.3285556 0.1362794
## 3 0.31447365 0.2051794 0.2574410 0.2229060
## 4 0.08262368 0.3840338 0.1441641 0.3891784
## 5 0.24082876 0.2502410 0.2149044 0.2940259
## 6 0.18516034 0.2852671 0.1842011 0.3453714head(plda$x, 3)## LD1 LD2
## 1 1.0222251 -0.3068424
## 2 1.2303376 0.2122278
## 3 0.4500579 -0.4699968plot(r)
plot(r3)
r <- lda(quality ~ .,
train_df,
prior = prior)
prop.lda = r$svd^2/sum(r$svd^2)
plda <- predict(object = r,
newdata = df)dataset = data.frame(species = df[,"quality"],lda = plda$x)
ggplot(dataset) + geom_point(aes(lda.LD1, lda.LD2, colour = quality, shape = quality), size = 2.5) + labs(x = paste("LD1 (", percent(prop.lda[1]), ")", sep=""),y = paste("LD2 (", percent(prop.lda[2]), ")", sep=""))
# lets play with accuracy
# lets look at another way to divide a dataset
set.seed(101) # Nothing is random!!
sample_n(df,10)## # A tibble: 10 × 3
## alcohol `volatile acidity` quality
## <dbl> <dbl> <chr>
## 1 12.4 0.33 Very Good
## 2 9.2 0.415 Average
## 3 10.5 0.51 Good
## 4 11.2 0.48 Average
## 5 9.2 0.58 Average
## 6 9.6 0.55 Average
## 7 11.1 0.34 Average
## 8 9 0.52 Good
## 9 10.2 0.45 Good
## 10 9.4 0.46 Average# Lets take a sample of 75/25 like before. Dplyr preserves class.
training_sample <- sample(c(TRUE, FALSE), nrow(df), replace = T, prob = c(0.75,0.25))
train <- df[training_sample, ]
test <- df[!training_sample, ]
#lets run LDA like before
lda.df <- lda(quality ~ ., train)
# do a quick plot to understand how good the model is
plot(lda.df, col = as.integer(train$quality))## Warning in eqscplot(X[, 1L:2L], xlab = xlab, ylab = ylab, type = "n", ...): NAs
## introduced by coercion
# Sometime bell curves are better
# Set outer margin to zero
par(oma = c(0, 0, 0, 0))
# Set inner margin to a smaller value
par(mar = c(2, 2, 2, 2))# Set outer margin to zero
par(oma = c(0, 0, 0, 0))
# Set inner margin to a smaller value
par(mar = c(2, 2, 2, 2))
# Set the plot region directly
par(plt = c(0.1, 0.9, 0.1, 0.9))
# Plot the LDA result
plot(lda.df, dimen = 1, type = "b")
# Reset plotting parameters
par(oma = c(0, 0, 0, 0))
par(mar = c(5, 4, 4, 2) + 0.1) # Reset to default valuesattach(train)## The following objects are masked from df (pos = 3):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 9):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 10):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 11):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 12):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 24):
##
## alcohol, quality, volatile acidity## The following objects are masked from df (pos = 27):
##
## alcohol, quality, volatile aciditytrain## # A tibble: 861 × 3
## alcohol `volatile acidity` quality
## <dbl> <dbl> <chr>
## 1 9.8 0.88 Average
## 2 9.8 0.76 Average
## 3 9.4 0.66 Average
## 4 9.4 0.6 Average
## 5 10 0.65 Very Good
## 6 9.5 0.58 Very Good
## 7 9.2 0.58 Average
## 8 9.9 0.615 Average
## 9 9.1 0.61 Average
## 10 10.5 0.28 Very Good
## # ℹ 851 more rows- Residual Analysis
The residual analysis for the predictor variable “volatile acidity” reveals significant variability in the response variable “quality” that is not explained by this predictor.
The residual sum of squares (SS) is 30.4343, indicating the total unexplained variability in the response variable after accounting for “volatile acidity.”
The mean square (MS) represents the average unexplained variability per degree of freedom, which is 0.02677.
The associated F-value (F) is 47.938, indicating that there is a significant difference in the mean response variable among the groups defined by “quality.”
The p-value (Pr(>F)) is less than 2.2e-16, indicating strong evidence against the null hypothesis. This suggests that “volatile acidity” significantly influences the quality of the product.
Overall, the residual analysis underscores the importance of “volatile acidity” as a predictor of product quality. However, it also implies that other factors not included in the model may contribute to the remaining variability in product quality.
Further investigation or refinement of the model may be necessary to capture these additional influences.
- Model Accuracy (2 points) Df (Degrees of Freedom): For the factor “quality”: 5 degrees of freedom, representing the number of quality levels minus 1.
For residuals: 1137 degrees of freedom, representing the error or unexplained variability.
Sum Sq (Sum of Squares):
For the factor “quality”: 356.83, which is the variability in alcohol content explained by the quality levels. For residuals: 980.62, which is the unexplained variability or error.
Mean Sq (Mean Square):
For the factor “quality”: 71.366, which is the average amount of variability in alcohol content explained by the quality levels.
For residuals: 0.862, which is the average amount of unexplained variability or error.
F value (F-statistic):
This value tests the null hypothesis that there is no relationship between alcohol content and quality levels. The obtained F value of 82.747 is highly significant (p < 0.05), indicating strong evidence against the null hypothesis.
Pr(>F) (p-value):
This is the probability of observing an F statistic as extreme as the one obtained if the null hypothesis were true.
The very small p-value (< 2.2e-16) indicates strong evidence against the null hypothesis, suggesting a significant relationship between alcohol content and quality levels.
Overall, the ANOVA results suggest that alcohol content significantly influences the quality levels of the observations.
# Reset to default values
# THis plot shows the essense of LDA. It puts everything on a line and finds cutoffs.
# Partition plots
# Print the column names of the train data frame
train$quality <- factor(train$quality)
partimat(train$quality ~ train$alcohol + train$`volatile acidity` , data=train, method="lda")
# Lets focus on accuracy. Table function
lda.train <- predict(lda.df)
train$lda <- lda.train$class
table(train$lda,train$quality)##
## Average Bad Excellent Good Poor Very Good
## Average 283 3 0 132 22 5
## Bad 1 1 0 0 1 0
## Excellent 0 0 0 0 0 0
## Good 77 0 5 186 6 62
## Poor 0 0 0 0 0 0
## Very Good 4 0 4 31 1 37# running accuracy on the training set shows how good the model is. It is not an indication of "true" accuracy. We will use the test set to approximate accuracy
lda.test <- predict(lda.df,test)
test$lda <- lda.test$class
table(test$lda,test$quality)##
## Average Bad Excellent Good Poor Very Good
## Average 92 2 0 44 3 1
## Bad 3 0 0 0 0 0
## Excellent 0 0 0 0 0 0
## Good 22 0 3 54 0 24
## Poor 0 0 0 0 0 0
## Very Good 1 0 4 15 0 14# Wilk's Lambda and F test for each variablw
m <- manova(cbind(alcohol,`volatile acidity`)~quality,data=df)
summary(m,test="Wilks")## Df Wilks approx F num Df den Df Pr(>F)
## quality 5 0.63168 58.665 10 2272 < 0.00000000000000022 ***
## Residuals 1137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m,test="Pillai")## Df Pillai approx F num Df den Df Pr(>F)
## quality 5 0.37817 53.024 10 2274 < 0.00000000000000022 ***
## Residuals 1137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.aov(m)## Response alcohol :
## Df Sum Sq Mean Sq F value Pr(>F)
## quality 5 356.83 71.366 82.747 < 0.00000000000000022 ***
## Residuals 1137 980.62 0.862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response volatile acidity :
## Df Sum Sq Mean Sq F value Pr(>F)
## quality 5 6.4158 1.28317 47.938 < 0.00000000000000022 ***
## Residuals 1137 30.4343 0.02677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1- Learnings and Takeaways (20 points)
Positive Attributes (Positive Correlation with Quality):
Alcohol: Generally indicates that higher alcohol levels might be associated with better quality wines.
Sulphates: A slight positive relationship with quality may suggest that sulphates contribute favorably to certain aspects of wine preservation that are appreciated in quality wines.
Free Sulfur Dioxide: A mild positive link suggests a small favorable effect on quality, possibly through its role in maintaining freshness. Citric Acid: Weak positive association indicates it might have a slightly favorable impact on wine’s taste and perceived quality.
Negative Attributes (Negative Correlation with Quality):
Volatile Acidity: A negative association suggests that higher levels are typically found in lower quality wines due to the potential for undesirable flavors.
Density: Seems to have a negative impact, where higher density may be associated with lower quality wines. Fixed Acidity: Slight negative correlation may imply that higher levels of fixed acidity aren’t typically preferred in quality wines.
Neutral Attributes (Minimal or No Correlation with Quality):
pH: The correlation is so minimal that pH levels seem to have a negligible impact on quality in this context. Total Sulfur Dioxide: Very low positive correlation, not significantly affecting quality perception.
Chlorides: Very weak correlation, indicating that the presence of chlorides does not have a strong influence on the quality. Residual
Sugar: Has a very low correlation, suggesting that sugar levels do not play a major role in determining the quality of these wines.